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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 33066

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Special Issue Editors

Prof. Dr. Anna Maria Almerico

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Guest Editor

Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
Interests: medicinal chemistry; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Tutone

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Guest Editor

Department of Biological, Chemical and Pharmaceutical Science and Technology (STEBICEF), University of Palermo, Palermo, Italy
Interests: oxidative stress; nutraceuticals; anticancer drugs; medicinal chemistry; drug design and discovery; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking; HTVS; cystic fibrosis translational readthrough inducing drugs (TRIDs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to inform you that Molecules will be launch the second part of the Special Issue “Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics“ https://0-www-mdpi-com.brum.beds.ac.uk/journal/molecules/special_issues/comput_appr_drug_dis_des_med_chem_bio.

Understanding ligand–receptor interaction is a key feature in all research fields related to drug design and discovery. This issue will focus on computational approaches that can improve the development of in silico methodologies, a primary need in pharmaceutical sciences. The ongoing and widespread discovery of new biological targets suitable for therapeutic intervention should be paralleled by a high and fast development of newly discovered ligands (potential drugs) or the repurposing of an old drug for the treatment of new diseases.

In this light, all computational approaches, such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, and reverse modeling, represent efficient tools to obtain insights into structure–function relationships for small molecules and/or natural compounds, also including ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR, extensively used in hit/lead identification and optimization.

Let us also not forget that drug design and the development process strive to predict the metabolic fate of a drug candidate to establish a relationship between the pharmacodynamics and pharmacokinetics and to highlight the potential toxicity of the drug candidate. In recent years, the improvement of in silico approaches has allowed researchers to obtain more reliable data. As such, this Special Issue welcomes submissions from researchers in the field of computational drug discovery and design, including original research and review articles related to pharmaceutical sciences, pharmacology, chemical biology, and bioinformatics.

Papers combining both experimental and computational studies are also encouraged.

Prof. Dr. Anna Maria Almerico
Prof. Dr. Marco Tutone
Guest Editors

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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 submissions that pass pre-check are 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. Molecules is an international peer-reviewed open access semimonthly 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 2700 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

QSAR and 3D-QSAR
Unbiased and biased molecular dynamics
Pharmacophore modeling
Reverse modeling
Ab initio calculations
Protein–protein interactions
Free energy profiling
Modeling of nucleic acids (mRNA, rRNA, tRNA)
Molecular docking
Virtual screening
Multitarget approaches
ADMET prediction
Similarity analysis
Computational approaches applied to natural compounds

Published Papers (13 papers)

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Editorial

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4 pages, 172 KiB

Open AccessEditorial

Computational Approaches and Drug Discovery: Where Are We Going?

by Marco Tutone and Anna Maria Almerico

Molecules 2024, 29(5), 969; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules29050969 - 22 Feb 2024

Viewed by 451

Abstract

Science is a point of view [...] Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

Research

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12 pages, 13438 KiB

Open AccessArticle

Computational Analysis of Histamine Protonation Effects on H₁R Binding

by Marcus Conrad, Anselm H. C. Horn and Heinrich Sticht

Molecules 2023, 28(9), 3774; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28093774 - 27 Apr 2023

Cited by 1 | Viewed by 1253

Abstract

Despite numerous studies investigating histamine and its receptors, the impact of histamine protonation states on binding to the histamine H

_{1}

-receptor (H

_{1}

R) has remained elusive. Therefore, we assessed the influence of different histamine tautomers (

τ

-tautomer,

π

-tautomer) and [...] Read more.

Despite numerous studies investigating histamine and its receptors, the impact of histamine protonation states on binding to the histamine H

_{1}

-receptor (H

_{1}

R) has remained elusive. Therefore, we assessed the influence of different histamine tautomers (

τ

-tautomer,

π

-tautomer) and charge states (mono- vs. dicationic) on the interaction with the ternary histamine-H

_{1}

R-G_q complex. In atomistic molecular dynamics simulations, the

τ

-tautomer formed stable interactions with the receptor, while the

π

-tautomer induced a rotation of the histamine ring by 180° and formed only weaker hydrogen bonding interactions. This suggests that the

τ

-tautomer is more relevant for stabilization of the active ternary histamine-H

_{1}

R-G_q complex. In addition to the two monocationic tautomers, the binding of dicationic histamine was investigated, whose interaction with the H

_{1}

R had been observed in a previous experimental study. Our simulations showed that the dication is less compatible with the ternary histamine-H

_{1}

R-G_q complex and rather induces an inactive conformation in the absence of the G_q protein. Our data thus indicate that the charge state of histamine critically affects its interactions with the H

_{1}

R. Ultimately these findings might have implications for the future development of new ligands that stabilize distinct H

_{1}

R activation states. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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20 pages, 5306 KiB

Open AccessArticle

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

by Elmeri M. Jokinen, Miika Niemeläinen, Sami T. Kurkinen, Jukka V. Lehtonen, Sakari Lätti, Pekka A. Postila, Olli T. Pentikäinen and Sanna P. Niinivehmas

Molecules 2023, 28(8), 3420; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28083420 - 13 Apr 2023

Cited by 3 | Viewed by 1673

Abstract

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target’s binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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11 pages, 998 KiB

Open AccessArticle

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans ^†

by Freddy A. Bernal and Thomas J. Schmidt

Molecules 2023, 28(8), 3399; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28083399 - 12 Apr 2023

Cited by 2 | Viewed by 1280

Abstract

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure–activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure–activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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15 pages, 4131 KiB

Open AccessArticle

Targeting Toxoplasma gondii ME49 TgAPN2: A Bioinformatics Approach for Antiparasitic Drug Discovery

by Ali Altharawi

Molecules 2023, 28(7), 3186; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28073186 - 03 Apr 2023

Viewed by 1338

Abstract

As fewer therapeutic options are available for treating toxoplasmosis, newer antiparasitic drugs that can block TgAPN2 M1 aminopeptidase are of significant value. Herein, we employed several computer-aided drug-design approaches with the objective of identifying drug molecules from the Asinex library with stable conformation and binding energy scores. By a structure-based virtual screening process, three molecules—LAS_52160953, LAS_51177972, and LAS_52506311—were identified as promising candidates, with binding affinity scores of −8.6 kcal/mol, −8.5 kcal/mol, and −8.3 kcal/mol, respectively. The compounds produced balanced interacting networks of hydrophilic and hydrophobic interactions, vital for holding the compounds at the docked cavity and stable binding conformation. The docked compound complexes with TgAPN2 were further subjected to molecular dynamic simulations that revealed mean RMSD for the LAS_52160953 complex of 1.45 Å), LAS_51177972 complex 1.02 Å, and LAS_52506311 complex 1.087 Å. Another round of binding free energy validation by MM-GBSA/MM-PBSA was done to confirm docking and simulation findings. The analysis predicted average MM-GBSA value of <−36 kcal/mol and <−35 kcal/mol by MM-PBSA. The compounds were further classified as appropriate candidates to be used as drug-like molecules and showed favorable pharmacokinetics. The shortlisted compounds showed promising biological potency against the TgAPN2 enzyme and may be used in experimental validation. They may also serve as parent structures to design novel derivatives with enhanced biological potency. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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18 pages, 9346 KiB

Open AccessArticle

Fighting Antibiotic Resistance: New Pyrimidine-Clubbed Benzimidazole Derivatives as Potential DHFR Inhibitors

by M. Akiful Haque, Akash Marathakam, Ritesh Rana, Samar J Almehmadi, Vishal B. Tambe, Manoj S. Charde, Fahadul Islam, Falak A. Siddiqui, Giulia Culletta, Anna Maria Almerico, Marco Tutone and Sharuk L. Khan

Molecules 2023, 28(2), 501; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28020501 - 04 Jan 2023

Cited by 2 | Viewed by 1849

Abstract

The present work describes the design and development of seventeen pyrimidine-clubbed benzimidazole derivatives as potential dihydrofolate reductase (DHFR) inhibitors. These compounds were filtered by using ADMET, drug-likeness characteristics calculations, and molecular docking experiments. Compounds 27, 29, 30, 33, 37, 38, and 41 were chosen for the synthesis based on the results of the in silico screening. Each of the synthesized compounds was tested for its in vitro antibacterial and antifungal activities using a variety of strains. All the compounds showed antibacterial properties against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus pyogenes) as well as Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa). Most of the compounds either had a higher potency than chloramphenicol or an equivalent potency to ciprofloxacin. Compounds 29 and 33 were effective against all the bacterial and fungal strains. Finally, the 1,2,3,4-tetrahydropyrimidine-2-thiol derivatives with a 6-chloro-2-(chloromethyl)-1H-benzo[d]imidazole moiety are potent enough to be considered a promising lead for the discovery of an effective antibacterial agent. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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15 pages, 2616 KiB

Open AccessArticle

Deciphering the Potential of Pre and Pro-Vitamin D of Mushrooms against Mpro and PLpro Proteases of COVID-19: An In Silico Approach

by Abhay Tiwari, Garima Singh, Gourav Choudhir, Mohit Motiwale, Nidhi Joshi, Vasudha Sharma, Rupesh K. Srivastava, Satyawati Sharma, Marco Tutone and Pradeep Kumar Singour

Molecules 2022, 27(17), 5620; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27175620 - 31 Aug 2022

Cited by 2 | Viewed by 1951

Abstract

Vitamin D’s role in combating the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, has been established in unveiling viable inhibitors of COVID-19. The current study investigated the role of pre and pro-vitamin D bioactives from edible mushrooms against Mpro and PLpro proteases of SARS-CoV-2 by computational experiments. The bioactives of mushrooms, specifically ergosterol (provitamin D₂), 7-dehydrocholesterol (provitamin-D₃), 22,23-dihydroergocalciferol (provitamin-D₄), cholecalciferol (vitamin-D₃), and ergocalciferol (vitamin D₂) were screened against Mpro and PLpro. Molecular docking analyses of the generated bioactive protease complexes unravelled the differential docking energies, which ranged from −7.5 kcal/mol to −4.5 kcal/mol. Ergosterol exhibited the lowest binding energy (−7.5 kcal/mol) against Mpro and PLpro (−5.9 kcal/mol). The Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) and MD simulation analyses indicated that the generated complexes were stable, thus affirming the putative binding of the bioactives to viral proteases. Considering the pivotal role of vitamin D bioactives, their direct interactions against SARS-CoV-2 proteases highlight the promising role of bioactives present in mushrooms as potent nutraceuticals against COVID-19. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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16 pages, 3740 KiB

Open AccessArticle

Virtual Screening for FDA-Approved Drugs That Selectively Inhibit Arginase Type 1 and 2

by Trishna Saha Detroja and Abraham O. Samson

Molecules 2022, 27(16), 5134; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27165134 - 12 Aug 2022

Cited by 8 | Viewed by 2913

Abstract

Arginases are often overexpressed in human diseases, and they are an important target for developing anti-aging and antineoplastic drugs. Arginase type 1 (ARG1) is a cytosolic enzyme, and arginase type 2 (ARG2) is a mitochondrial one. In this study, a dataset containing 2115-FDA-approved drug molecules is virtually screened for potential arginase binding using molecular docking against several ARG1 and ARG2 structures. The potential arginase ligands are classified into three categories: (1) Non-selective, (2) ARG1 selective, and (3) ARG2 selective. The evaluated potential arginase ligands are then compared with their clinical use. Remarkably, half of the top 30 potential drugs are used clinically to lower blood pressure and treat cancer, infection, kidney disease, and Parkinson’s disease thus partially validating our virtual screen. Most notable are the antihypertensive drugs candesartan, irbesartan, indapamide, and amiloride, the antiemetic rolapitant, the anti-angina ivabradine, and the antidiabetic metformin which have minimal side effects. The partial validation also favors the idea that the other half of the top 30 potential drugs could be used in therapeutic settings. The three categories greatly expand the selectivity of arginase inhibition. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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12 pages, 2992 KiB

Open AccessArticle

Identification and Evaluation of Traditional Chinese Medicine Natural Compounds as Potential Myostatin Inhibitors: An In Silico Approach

by Shahid Ali, Khurshid Ahmad, Sibhghatulla Shaikh, Jeong Ho Lim, Hee Jin Chun, Syed Sayeed Ahmad, Eun Ju Lee and Inho Choi

Molecules 2022, 27(13), 4303; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27134303 - 04 Jul 2022

Cited by 12 | Viewed by 2488

Abstract

Myostatin (MSTN), a negative regulator of muscle mass, is reported to be increased in conditions linked with muscle atrophy, sarcopenia, and other muscle-related diseases. Most pharmacologic approaches that treat muscle disorders are ineffective, emphasizing the emergence of MSTN inhibition. In this study, we used computational screening to uncover natural small bioactive inhibitors from the Traditional Chinese Medicine database (~38,000 compounds) for the MSTN protein. Potential ligands were screened, based on binding affinity (150), physicochemical (53) and ADMET properties (17). We found two hits (ZINC85592908 and ZINC85511481) with high binding affinity and specificity, and their binding patterns with MSTN protein. In addition, molecular dynamic simulations were run on each complex to better understand the interaction mechanism of MSTN with the control (curcumin) and the hit compounds (ZINC85592908 and ZINC85511481). We determined that the hits bind to the active pocket site (Helix region) and trigger conformational changes in the MSTN protein. Since the stability of the ZINC85592908 compound was greater than the MSTN control, we believe that ZINC85592908 has therapeutic potential against the MSTN protein and may hinder downstream singling by inhibiting the MSTN protein and increasing myogenesis in the skeletal muscle tissues. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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18 pages, 3083 KiB

Open AccessArticle

Unveiling of Pyrimidindinones as Potential Anti-Norovirus Agents—A Pharmacoinformatic-Based Approach

by Oluwakemi Ebenezer, Nkululeko Damoyi, Maryam A. Jordaan and Michael Shapi

Molecules 2022, 27(2), 380; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27020380 - 07 Jan 2022

Cited by 8 | Viewed by 1646

Abstract

The RNA-dependent RNA polymerase (RdRp) receptor is an attractive target for treating human norovirus (HNV). A computer-aided approach like e-pharmacophore, molecular docking, and single point energy calculations were performed on the compounds retrieved from the Development Therapeutics Program (DTP) AIDS Antiviral Screen Database to identify the antiviral agent that could target the HNV RdRp receptor. Induced-fit docking (IFD) results showed that compounds ZINC1617939, ZINC1642549, ZINC6425208, ZINC5887658 and ZINC32068149 bind with the residues in the active site-B of HNV RdRp receptor via hydrogen bonds, salt bridge, and electrostatic interactions. During the molecular dynamic simulations, compounds ZINC6425208, ZINC5887658 and ZINC32068149 displayed an unbalanced backbone conformation with HNV RdRp protein, while ZINC1617939 and ZINC1642549 maintained stability with the protein backbone when interacting with the residues. Hence, the two new concluding compounds discovered by the computational approach can be used as a chemotype to design promising antiviral agents aimed at HNV RdRp. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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Review

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20 pages, 3330 KiB

Open AccessReview

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

by Davide Bassani and Stefano Moro

Molecules 2023, 28(9), 3906; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28093906 - 05 May 2023

Cited by 21 | Viewed by 5351 | Correction

Abstract

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of “computer-aided drug design” (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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15 pages, 1415 KiB

Open AccessReview

eEF2K Inhibitor Design: The Progression of Exemplary Structure-Based Drug Design

by Kody A. Klupt and Zongchao Jia

Molecules 2023, 28(3), 1095; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28031095 - 21 Jan 2023

Cited by 1 | Viewed by 2014

Abstract

The α-kinase, eEF2K, phosphorylates the threonine 56 residue of eEF2 to inhibit global peptide elongation (protein translation). As a master regulator of protein synthesis, in combination with its unique atypical kinase active site, investigations into the targeting of eEF2K represents a case of intense structure-based drug design that includes the use of modern computational techniques. The role of eEF2K is incredibly diverse and has been scrutinized in several different diseases including cancer and neurological disorders—with numerous studies inhibiting eEF2K as a potential treatment option, as described in this paper. Using available crystal structures of related α-kinases, particularly MHCKA, we report how homology modeling has been used to improve inhibitor design and efficacy. This review presents an overview of eEF2K related drug discovery efforts predating from the 1990’s, to more recent in vivo studies in rat models. We also provide the reader with a basic introduction to several approaches and software programs used to undertake such drug discovery campaigns. With the recent exciting publication of an eEF2K crystal structure, we present our view regarding the future of eEF2K drug discovery. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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17 pages, 2303 KiB

Open AccessReview

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

by Júlia Dulsat, Blanca López-Nieto, Roger Estrada-Tejedor and José I. Borrell

Molecules 2023, 28(2), 776; https://doi.org/10.3390/molecules28020776 - 12 Jan 2023

Cited by 26 | Viewed by 7187

Abstract

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski’s rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics II)

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