Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.

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Molecular docking: a powerful approach for structure-based drug discovery.

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Molecular Docking and Structure-Based Drug Design Strategies

Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions.

Results: We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score.

Contact:pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein–ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.

Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

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

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these We next discuss reported comparative studies, outlining criteria for their interpretation In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method

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Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

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Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go