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Artificial Intelligence and Computer Aided Drug Design

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 22896

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Prof. Andrea Danani

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Guest Editor

Istituto Dalle Molle Di Studi Sull'intelligenza Artificiale, Manno, Switzerland
Interests: Virtual screening and CADD; Machine learning in molecular simulation data; Protein conformational changes related to drug interactions; In-silico protein–ligand binding, kinetics and thermodynamics; AI in pharmaceutical and clinical data analysis

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is gaining more and more importance in the pharmaceutical sector, deeply transforming the drug discovery process. There are many potential benefits of applying AI techniques to improve the development of new molecules and the identification of new targets, cutting R&D costs and time.

In drug discovery, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the huge amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs, which can be repurposed for an alternative use in medicine.

Moreover, with the help of AI, it becomes easier to run clinical tests, diagnose diseases and provide the most effective treatment for a particular disease. As it can interpret test results, AI can also look through various sources including publications to correctly diagnose critical ailments.

In this Special Issue of the International Journal of Molecular Sciences, we would like to discuss new approaches based on AI in the drug discovery process and in the repositioning of old molecules, together with their impact on the pharmaceutical pipeline. The goal is to provide an overview of the sectors where AI might play a crucial role in the pharmaceutical world in the next years.

Prof. Andrea Danani
Guest Editor

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Keywords

Computer aided drug design
Machine learning, deep learning
Big data
Virtual screening
Drug discovery and repurposing
Drug-target interaction

Published Papers (4 papers)

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Research

11 pages, 2247 KiB

Open AccessArticle

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients

by Minjae Joo, Aron Park, Kyungdoc Kim, Won-Joon Son, Hyo Sug Lee, GyuTae Lim, Jinhyuk Lee, Dae Ho Lee, Jungsuk An, Jung Ho Kim, TaeJin Ahn and Seungyoon Nam

Int. J. Mol. Sci. 2019, 20(24), 6276; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20246276 - 12 Dec 2019

Cited by 20 | Viewed by 5449

Abstract

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computer Aided Drug Design)

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22 pages, 6562 KiB

Open AccessArticle

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

by Yu Wei, Wei Li, Tengfei Du, Zhangyong Hong and Jianping Lin

Int. J. Mol. Sci. 2019, 20(14), 3572; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20143572 - 22 Jul 2019

Cited by 22 | Viewed by 3785

Abstract

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computer Aided Drug Design)

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17 pages, 4748 KiB

Open AccessArticle

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction

by Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois and Jie Fan

Int. J. Mol. Sci. 2019, 20(14), 3389; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20143389 - 10 Jul 2019

Cited by 112 | Viewed by 8909

Abstract

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R² values compared with the Cubist benchmark. The median R² increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computer Aided Drug Design)

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9 pages, 620 KiB

Open AccessCommunication

Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding

by Yang Yang, Huiwen Zheng, Chunhua Wang, Wanyue Xiao and Taigang Liu

Int. J. Mol. Sci. 2019, 20(9), 2344; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20092344 - 11 May 2019

Cited by 3 | Viewed by 3180

Abstract

To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computer Aided Drug Design)

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