Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/
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ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

https://www.beilstein-journals.org/bjoc/content/pdf/1860-5397-12-267.pdf

Computational methods in drug discovery.

Gallic acid: Pharmacological activities and molecular mechanisms involved in inflammation-related diseases.

The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure-property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R(2) = 0.97, RMSEF = 0.12, Q(2) = 0.83, RMSECV = 0.31 for the training set and RT(2) = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.

ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

Lipophilicity, evaluated by either n-octanol/water partition coefficient or n-octanol/buffer solution distribution coefficient, is of high importance in pharmacology, toxicology, and medicinal chemistry. A quantitative structure– property relationship study was carried out to predict distribution coefficients at pH7.4 (logD7.4) of a large data set consisting of 1130 organic compounds. Partial least squares and support vector machine (SVM) regressions were employed to build prediction models with 30 molecular descriptors selected by genetic algorithm. The obtained results demonstrated that the SVM model is more reliable and has a better prediction performance than the partial least squares model. The square correlation coefficients of fitting, cross validation, and prediction are 0.92, 0.90, and 0.89, respectively. The corresponding root mean square errors are 0.52, 0.59, and 0.56, respectively. The robustness, reliability, and generalization ability of the model were assessed by Y-randomization test and applicability domain. When compared with logD7.4 values calculated by five existing methods from Discovery Studio and ChemAxon, our SVM model shows superiority over them. The results indicated that our model could give a reliable and robust prediction of logD7.4. Copyright © 2015 John Wiley & Sons, Ltd. Supporting information may be found in the online version of this paper

Jian-Bing Wang

Papers

ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

In silico evaluation of logD7.4 and comparison with other prediction methods