Special Issue "In Silico Approaches in Drug Design"

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editor

Prof. Dr. Osvaldo Andrade Santos-Filho
E-Mail Website
Guest Editor
Center of Health Sciences, Laboratory of Molecular Modeling and Computational Structural Biology, Federal University of Rio de Janeiro, IPPN, Av. Carlos Chagas Filho 373, Bloco H, Rio de Janeiro RJ-21941-599, Brazil
Interests: molecular modeling; computational and medicinal chemistry; molecular simulations; structural biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few decades, computational methods have been successfully applied by the pharmaceutical community. This is mainly due to the development of both new theoretical approaches and new hardware and software technologies. In this context, in silico approaches such as molecular simulations, QM/MM simulations, chemoinformatics, artificial intelligence, etc., became fundamental in the drug design process. It can even be said that today it is impossible for a new drug to be invented without going through the “sieve” of in silico research. To celebrate the success story and advances in the important synergistic combination of drug design and in silico investigation, the journal Pharmaceuticals invites fellow scientists to submit original papers or reviews, which will be published in a Special Issue on “In silico Approaches in Drug Design 2021”. Such an issue will contemplate the following topics: computer-aided drug design, molecular dynamics simulations, Monte Carlo simulations, QM/MM simulations, molecular docking, chemoinformatics, in silico databases, data mining, machine learning, pharmacophore-based virtual screening, combinatorial chemistry, QSAR, and in silico ADMET.

Looking forward to your contribution.

Prof. Dr. Osvaldo Andrade Santos-Filho
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computer-aided drug design
  • Molecular dynamics simulations
  • Monte Carlo simulations
  • QM/MM simulations
  • Molecular docking
  • Chemoinformatics
  • In silico database
  • Data mining
  • Machine learning
  • Pharmacophore-based virtual screening
  • Combinatorial chemistry
  • QSAR
  • In silico ADMET

Published Papers (20 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems
Pharmaceuticals 2022, 15(2), 132; https://0-doi-org.brum.beds.ac.uk/10.3390/ph15020132 - 22 Jan 2022
Viewed by 334
Abstract
DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular [...] Read more.
DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆Tm value, which found a 0.84 R2 score. Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,N’-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (51) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆Tm experimental values of DNA intercalating agents. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Article
In Silico Design, Synthesis, and Biological Evaluation of Anticancer Arylsulfonamide Endowed with Anti-Telomerase Activity
Pharmaceuticals 2022, 15(1), 82; https://0-doi-org.brum.beds.ac.uk/10.3390/ph15010082 - 10 Jan 2022
Viewed by 144
Abstract
Telomerase, a reverse transcriptase enzyme involved in DNA synthesis, has a tangible role in tumor progression. Several studies have evidenced telomerase as a promising target for developing cancer therapeutics. The main reason is due to the overexpression of telomerase in cancer cells (85–90%) [...] Read more.
Telomerase, a reverse transcriptase enzyme involved in DNA synthesis, has a tangible role in tumor progression. Several studies have evidenced telomerase as a promising target for developing cancer therapeutics. The main reason is due to the overexpression of telomerase in cancer cells (85–90%) compared with normal cells where it is almost unexpressed. In this paper, we used a structure-based approach to design potential inhibitors of the telomerase active site. The MYSHAPE (Molecular dYnamics SHared PharmacophorE) approach and docking were used to screen an in-house library of 126 arylsulfonamide derivatives. Promising compounds were synthesized using classical and green methods. Compound 2C revealed an interesting IC50 (33 ± 4 µM) against the K-562 cell line compared with the known telomerase inhibitor BIBR1532 IC50 (208 ± 11 µM) with an SI ~10 compared to the BALB/3-T3 cell line. A 100 ns MD simulation of 2C in the telomerase active site evidenced Phe494 as the key residue as well as in BIBR1532. Each moiety of compound 2C was involved in key interactions with some residues of the active site: Arg557, Ile550, and Gly553. Compound 2C, as an arylsulfonamide derivative, is an interesting hit compound that deserves further investigation in terms of optimization of its structure to obtain more active telomerase inhibitors Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
A Review on Parallel Virtual Screening Softwares for High-Performance Computers
Pharmaceuticals 2022, 15(1), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/ph15010063 - 04 Jan 2022
Viewed by 151
Abstract
Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, [...] Read more.
Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Discovery of Small Molecules as Membrane-Bound Catechol-O-methyltransferase Inhibitors with Interest in Parkinson’s Disease: Pharmacophore Modeling, Molecular Docking and In Vitro Experimental Validation Studies
Pharmaceuticals 2022, 15(1), 51; https://0-doi-org.brum.beds.ac.uk/10.3390/ph15010051 - 31 Dec 2021
Viewed by 223
Abstract
A pharmacophore-based virtual screening methodology was used to discover new catechol-O-methyltransferase (COMT) inhibitors with interest in Parkinson’s disease therapy. To do so, pharmacophore models were constructed using the structure of known inhibitors and then they were used in a screening in [...] Read more.
A pharmacophore-based virtual screening methodology was used to discover new catechol-O-methyltransferase (COMT) inhibitors with interest in Parkinson’s disease therapy. To do so, pharmacophore models were constructed using the structure of known inhibitors and then they were used in a screening in the ZINCPharmer database to discover hit molecules with the desired structural moieties and drug-likeness properties. Following this, the 50 best ranked molecules were submitted to molecular docking to better understand their atomic interactions and binding poses with the COMT (PDB#6I3C) active site. Additionally, the hits’ ADMET properties were also studied to improve the obtained results and to select the most promising compounds to advance for in-vitro studies. Then, the 10 compounds selected were purchased and studied regarding their in-vitro inhibitory potency on human recombinant membrane-bound COMT (MBCOMT), as well as their cytotoxicity in rat dopaminergic cells (N27) and human dermal fibroblasts (NHDF). Of these, the compound ZIN27985035 displayed the best results: For MBCOMT inhibition an IC50 of 17.6 nM was determined, and low cytotoxicity was observed in both cell lines (61.26 and 40.32 μM, respectively). Therefore, the promising results obtained, combined with the structure similarity with commercial COMT inhibitors, can allow for the future development of a potential new Parkinson’s disease drug candidate with improved properties. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Unravelling the Interaction of Piperlongumine with the Nucleotide-Binding Domain of HSP70: A Spectroscopic and In Silico Study
Pharmaceuticals 2021, 14(12), 1298; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14121298 - 13 Dec 2021
Viewed by 490
Abstract
Piperlongumine (PPL) is an alkaloid extracted from several pepper species that exhibits anti-inflammatory and anti-carcinogenic properties. Nevertheless, the molecular mode of action of PPL that confers such powerful pharmacological properties remains unknown. From this perspective, spectroscopic methods aided by computational modeling were employed [...] Read more.
Piperlongumine (PPL) is an alkaloid extracted from several pepper species that exhibits anti-inflammatory and anti-carcinogenic properties. Nevertheless, the molecular mode of action of PPL that confers such powerful pharmacological properties remains unknown. From this perspective, spectroscopic methods aided by computational modeling were employed to characterize the interaction between PPL and nucleotide-binding domain of heat shock protein 70 (NBD/HSP70), which is involved in the pathogenesis of several diseases. Steady-state fluorescence spectroscopy along with time-resolved fluorescence revealed the complex formation based on a static quenching mechanism. Van’t Hoff analyses showed that the binding of PPL toward NBD is driven by equivalent contributions of entropic and enthalpic factors. Furthermore, IDF and Scatchard methods applied to fluorescence intensities determined two cooperative binding sites with Kb of (6.3 ± 0.2) × 104 M−1. Circular dichroism determined the thermal stability of the NBD domain and showed that PPL caused minor changes in the protein secondary structure. Computational simulations elucidated the microenvironment of these interactions, showing that the binding sites are composed mainly of polar amino acids and the predominant interaction of PPL with NBD is Van der Waals in nature. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Fragment-Based Ligand Discovery Applied to the Mycolic Acid Methyltransferase Hma (MmaA4) from Mycobacterium tuberculosis: A Crystallographic and Molecular Modelling Study
Pharmaceuticals 2021, 14(12), 1282; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14121282 - 08 Dec 2021
Viewed by 513
Abstract
The mycolic acid biosynthetic pathway represents a promising source of pharmacological targets in the fight against tuberculosis. In Mycobacterium tuberculosis, mycolic acids are subject to specific chemical modifications introduced by a set of eight S-adenosylmethionine dependent methyltransferases. Among these, Hma (MmaA4) is [...] Read more.
The mycolic acid biosynthetic pathway represents a promising source of pharmacological targets in the fight against tuberculosis. In Mycobacterium tuberculosis, mycolic acids are subject to specific chemical modifications introduced by a set of eight S-adenosylmethionine dependent methyltransferases. Among these, Hma (MmaA4) is responsible for the introduction of oxygenated modifications. Crystallographic screening of a library of fragments allowed the identification of seven ligands of Hma. Two mutually exclusive binding modes were identified, depending on the conformation of residues 147–154. These residues are disordered in apo-Hma but fold upon binding of the S-adenosylmethionine (SAM) cofactor as well as of analogues, resulting in the formation of the short η1-helix. One of the observed conformations would be incompatible with the presence of the cofactor, suggesting that allosteric inhibitors could be designed against Hma. Chimeric compounds were designed by fusing some of the bound fragments, and the relative binding affinities of initial fragments and evolved compounds were investigated using molecular dynamics simulation and generalised Born and Poisson–Boltzmann calculations coupled to the surface area continuum solvation method. Molecular dynamics simulations were also performed on apo-Hma to assess the structural plasticity of the unliganded protein. Our results indicate a significant improvement in the binding properties of the designed compounds, suggesting that they could be further optimised to inhibit Hma activity. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Graphical abstract

Article
A Deep-Learning Proteomic-Scale Approach for Drug Design
Pharmaceuticals 2021, 14(12), 1277; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14121277 - 07 Dec 2021
Viewed by 624
Abstract
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures [...] Read more.
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
Pharmaceuticals 2021, 14(12), 1249; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14121249 - 30 Nov 2021
Cited by 1 | Viewed by 478
Abstract
Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel [...] Read more.
Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Graphical abstract

Article
A Rational Design of α-Helix-Shaped Peptides Employing the Hydrogen-Bond Surrogate Approach: A Modulation Strategy for Ras-RasGRF1 Interaction in Neuropsychiatric Disorders
Pharmaceuticals 2021, 14(11), 1099; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14111099 - 28 Oct 2021
Viewed by 555
Abstract
In the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation [...] Read more.
In the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation has been proposed as a potential target mechanism to attenuate ERK signalling in the brain. Previously, we showed that a cell penetrating peptide, RB3, was able to inhibit downstream signalling by preventing RasGRF1 (Ras guanine nucleotide-releasing factor 1), a neuronal specific GDP/GTP exchange factor, to bind Ras proteins, both in brain slices and in vivo, with an IC50 value in the micromolar range. The aim of this work was to mutate and improve this peptide through computer-aided techniques to increase its inhibitory activity against RasGRF1. The designed peptides were built based on the RB3 peptide structure corresponding to the α-helix of RasGRF1 responsible for Ras binding. For this purpose, the hydrogen-bond surrogate (HBS) approach was exploited to maintain the helical conformation of the designed peptides. Finally, residue scanning, MD simulations, and MM-GBSA calculations were used to identify 18 most promising α-helix-shaped peptides that will be assayed to check their potential activity against Ras-RasGRF1 and prevent downstream molecular events implicated in brain disorders. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Graphical abstract

Article
In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus
Pharmaceuticals 2021, 14(10), 1027; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14101027 - 09 Oct 2021
Viewed by 528
Abstract
Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The [...] Read more.
Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The proposed structures were subjected to docking calculations, molecular dynamics simulations, and free energy calculations, using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method, inside the active sites of VarTMPK and human TMPK (HssTMPK). The docking and molecular dynamic studies pointed to structures 2, 3, 4, 6, and 9 as more selective towards VarTMPK. In addition, the free energy data calculated through the MM-PBSA method, corroborated these results. This suggests that these compounds are potential selective inhibitors of VarTMPK and, thus, can be considered as template molecules to be synthesized and experimentally evaluated against smallpox. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes
Pharmaceuticals 2021, 14(10), 948; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14100948 - 22 Sep 2021
Viewed by 5526
Abstract
Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In [...] Read more.
Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach—DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of “hybrid” chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug–gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
High-Throughput Screening and Molecular Dynamics Simulation of Natural Product-like Compounds against Alzheimer’s Disease through Multitarget Approach
Pharmaceuticals 2021, 14(9), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14090937 - 18 Sep 2021
Cited by 4 | Viewed by 829
Abstract
Alzheimer’s disease (AD) is a progressive neurological disorder that affects 50 million people. Despite this, only two classes of medication have been approved by the FDA. Therefore, we have planned to develop therapeutics by multitarget approach. We have explored the library of 2029 [...] Read more.
Alzheimer’s disease (AD) is a progressive neurological disorder that affects 50 million people. Despite this, only two classes of medication have been approved by the FDA. Therefore, we have planned to develop therapeutics by multitarget approach. We have explored the library of 2029 natural product-like compounds for their multi-targeting potential against AD by inhibiting AChE, BChE (cholinergic pathway) MAO-A, and MOA-B (oxidative stress pathway) through in silico high-throughput screening and molecular dynamics simulation. Based on the binding energy of these target enzymes, approximately 189 compounds exhibited a score of less than −10 kcal/mol against all targets. However, none of the control inhibitors exhibited a binding affinity of less than −10 kcal/mol. Among these, the top 10 hits of compounds against all four targets were selected for ADME-T analysis. As a result, only F0850-4777 exhibited an acceptable range of physicochemical properties, drug-likeness, pharmacokinetics, and suitability for BBB permeation with high GI-A and non-toxic effects. The molecular dynamics study confirmed that F0850-4777 remained inside the binding cavity of targets in a stable conformation throughout the simulation and Prime-MM/GBSA study revealed that van der Waals’ energy (ΔGvdW) and non-polar solvation or lipophilic energy (ΔGSol_Lipo) contribute favorably towards the formation of a stable protein–ligand complex. Thus, F0850-4777 could be a potential candidate against multiple targets of two pathophysiological pathways of AD and opens the doors for further confirmation through in vitro and in vivo systems. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
In Silico Prediction of Novel Inhibitors of SARS-CoV-2 Main Protease through Structure-Based Virtual Screening and Molecular Dynamic Simulation
Pharmaceuticals 2021, 14(9), 896; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14090896 - 03 Sep 2021
Cited by 1 | Viewed by 1025
Abstract
The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (Mpro) of the virus is considered to [...] Read more.
The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (Mpro) of the virus is considered to be a promising drug target due to its crucial role in viral replication and its genomic dissimilarity to human proteases. In this study, we implemented a structure-based virtual screening (VS) protocol in search of compounds that could inhibit the viral Mpro. A library of >eight hundred compounds was screened by molecular docking into multiple structures of Mpro, and the result was analyzed by consensus strategy. Those compounds that were ranked mutually in the ‘Top-100’ position in at least 50% of the structures were selected and their analogous binding modes predicted simultaneously in all the structures were considered as bioactive poses. Subsequently, based on the predicted physiological and pharmacokinetic behavior and interaction analysis, eleven compounds were identified as ‘Hits’ against SARS-CoV-2 Mpro. Those eleven compounds, along with the apo form of Mpro and one reference inhibitor (X77), were subjected to molecular dynamic simulation to explore the ligand-induced structural and dynamic behavior of Mpro. The MM-GBSA calculations reflect that eight out of eleven compounds specifically possess high to good binding affinities for Mpro. This study provides valuable insights to design more potent and selective inhibitors of SARS-CoV-2 Mpro. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
Pharmaceuticals 2021, 14(8), 790; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14080790 - 11 Aug 2021
Cited by 1 | Viewed by 981
Abstract
In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use [...] Read more.
In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints
Pharmaceuticals 2021, 14(8), 758; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14080758 - 02 Aug 2021
Cited by 1 | Viewed by 1430
Abstract
Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training [...] Read more.
Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Rational Design of Novel Inhibitors of α-Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening
Pharmaceuticals 2021, 14(5), 482; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050482 - 19 May 2021
Viewed by 1086
Abstract
α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual [...] Read more.
α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r2 = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r2 (q2 = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r2 value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Article
Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics
Pharmaceuticals 2021, 14(3), 282; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14030282 - 21 Mar 2021
Cited by 5 | Viewed by 1155
Abstract
The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic [...] Read more.
The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Graphical abstract

Article
A New Computer Model for Evaluating the Selective Binding Affinity of Phenylalkylamines to T-Type Ca2+ Channels
by and
Pharmaceuticals 2021, 14(2), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020141 - 10 Feb 2021
Cited by 1 | Viewed by 735
Abstract
To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca2+ channels (TCCs), we created new homology models for both TCCs and a L-type calcium channel (LCC). We found that PAAs have a high affinity for domains [...] Read more.
To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca2+ channels (TCCs), we created new homology models for both TCCs and a L-type calcium channel (LCC). We found that PAAs have a high affinity for domains I and IV of TCCs and a low affinity for domains III and IV of the LCC. Therefore, they should be considered as favorable candidates for TCC blockers. The new homology models were validated with some commonly recognized TCC blockers that are well characterized. Additionally, examples of the TCC blockers created were also evaluated using these models. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Review

Jump to: Research

Review
Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design
Pharmaceuticals 2021, 14(10), 1062; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14101062 - 19 Oct 2021
Viewed by 1475
Abstract
We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock [...] Read more.
We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock and key” paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Figure 1

Review
In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management
Pharmaceuticals 2021, 14(8), 741; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14080741 - 29 Jul 2021
Viewed by 1069
Abstract
Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause [...] Read more.
Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause of CC, and despite the approval of prophylactic vaccines, there is no effective treatment for patients with pre-existing infections or HPV-induced carcinomas. High-risk (HR) HPV E6 and E7 oncoproteins are considered biomarkers in CC progression. Since the E6 structure was resolved, it has been one of the most studied targets to develop novel and specific therapeutics to treat/manage CC. Therefore, several small molecules (plant-derived or synthetic compounds) have been reported as blockers/inhibitors of E6 oncoprotein action, and computational-aided methods have been of high relevance in their discovery and development. In silico approaches have become a powerful tool for reducing the time and cost of the drug development process. Thus, this review will depict small molecules that are already being explored as HR HPV E6 protein blockers and in silico approaches to the design of novel therapeutics for managing CC. Besides, future perspectives in CC therapy will be briefly discussed. Full article
(This article belongs to the Special Issue In Silico Approaches in Drug Design)
Show Figures

Graphical abstract

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Computational Structure-Based Drug Design Approaches in SARS-CoV-2 Investigations
Authors: Osvaldo Andrade Santos-Filho
Affiliation: Laboratório de Modelagem Molecular e Biologia Estrutural Computacional, Instituto de Pesquisas de Produtos Naturais Walter Mors, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Bloco H, Cidade Universitária, 21941-902, Rio de Janeiro, RJ, Brazil
Abstract: At the end of 2019, a new strain of CoV of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) was identified to be the cause of a very contagious respiratory disease in China. The disease, known as COVID-19, rapidly spread around the world and became a global pandemic. Since then, scientists from all the world have worked tirelessly to develop medicines to prevent and treat the disease. Some emergency vaccines were developed and are now available, but up to now, no effective drug has been developed to treat people which were already infected by the disease. In this review, applications of some computational structure-based drug design approaches in COVID-19 studies are presented, including virtual screening by molecular docking, molecular dynamics simulations, and quantum enzymology by multiscale modeling.

Title: A multi-level computational approach to drug design: Particle informatics and process simulation study of a sildenafil nanocrystal formulation
Authors: Andreas Ouranidis
Affiliation: Aristotle University of Thessaloniki, Thessaloniki, Greece

Title: In silico structure-based screening of Philippine natural products against the SARS-CoV-2 (COVID-19) RNA-dependent RNA polymerase (RdRp)
Authors: Alexandra Isabelle D. Ang1, Maria Constancia O. Carrillo1, Junie B. Billones1, Marilen Parungao Balolong2, Lyre Anni E. Murao3, Stephani Joy Y. Macalino4*
Affiliation: 1Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; [email protected] (A.I.D.A.); [email protected] (M.C.O.C.); [email protected] (J.B.B.) 2Department of Biology, College of Arts and Sciences University of the Philippines Manila; [email protected] 3Department of Biological Sciences and Environmental Studies, College of Science and Mathematics, University of the Philippines Mindanao; [email protected] 4Chemistry Department, De La Salle University, 2401 Taft Avenue, Manila 0992, Philippines
Abstract: COVID-19 is a viral infection caused by SARS-CoV-2, an RNA virus related to the viruses which caused the SARS and MERS epidemics. The disease has affected approximately 114 million people worldwide to date, yet no drug has been approved for treatment. In the Southeast Asia, Philippines is one of the hardest hit countries of this deadly disease. RNA-dependent RNA polymerase (RdRp) has been identified as an attractive target for anti-viral treatment. Like a number of other RNA viruses, SARS-CoV-2 utilizes RdRp to facilitate the replication of its genome. For this reason, inhibition of this enzyme has been associated with the possibility of reduced viral loads in those affected. Computational tools such as molecular dynamics (MD) and molecular docking provide a way to perform virtual high-throughput screening before in vivo and in vitro studies and expedite the drug discovery process. This study aims to identify potential candidates from Philippine natural products that are able to inhibit SARS-Cov-2 RdRp through the use of computational methods.

Title: 2-Aminothiophene derivatives design by CADD exerts promising antileishmanial activity
Authors: Isadora Silva Luna, Francisco Jaime Bezerra Mendonça Junior, Klinger Antônio da Franca Rodrigues, Luciana Scotti, Eugene Muratov, Marcus Tullius Scotti
Affiliation: Universidade Estadual da Paraíba
Abstract: In this work, we performed the design, ADMET prediction, synthesis, and structure-activity relationship studies of new 2-aminothiophene derivatives (2AT) candidates for anti-leishmania drugs. Theoretical studies were carried out using the energies values (KJ/mol) obtained for each molecule in its Z and E configurations, and the prediction of cytotoxicity risks and ADMET properties. The selected 2AT were synthesized in good yields and their structures were confirmed by spectroscopic and spectrometric techniques. Leishmanicidal activity tests demonstrated that most of the compounds showed activity against promastigote and amastigote forms of Leishmania (Leishmania) amazonensis with IC50 values below 10µM, once again demonstrating the validity of CADD studies to predict compounds with promising leishmanicidal activity, and confirming that 2AT are privileged structures for the design of leishmanicidal agents.

Back to TopTop