Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
Highlights
The experimental screening of large libraries of compounds against panels of molecular targets, i.e., High-Throughput Screening (HTS), has represented the gold standard for discovering biologically active hits
They compared the performance of “Random Forest (RF)-Score” with that of other sixteen scoring functions implemented in currently available docking programs, demonstrating that it improved docking results both in virtual screening and lead optimization tasks and that its performances are independent from the employed training sets [121]
Considering the relevance of the topic and the improvements in: (i) currently available computational techniques and software in general, which allow to more accurately screen larger databases; (ii) hardware facilities, which enable a faster screening of ligands to targets, to a larger public, and; (iii) crystallography [36,37,38,39] and homology modeling [155,156] techniques, which allow expanding our knowledge on structural biology; we envision that further advances in reverse docking will certainly play a central role for target fishing and profiling of ligands in future drug discovery
Summary
The experimental screening of large libraries of compounds against panels of molecular targets, i.e., High-Throughput Screening (HTS), has represented the gold standard for discovering biologically active hits. The authors firstly developed an RF-based scoring function by using different sets of ligand-protein complexes with known activity data reported within the PDBbind database [122] They compared the performance of “RF-Score” with that of other sixteen scoring functions implemented in currently available docking programs, demonstrating that it improved docking results both in virtual screening and lead optimization tasks and that its performances are independent from the employed training sets [121]. Pereira et al [125] recently reported an approach based on Convolutional Neural Networks (CNN) called “DeepVS”, which learns the features relevant for the binding of a ligand to a target under study, given a set of docking results They firstly developed a scoring function that generates DL-based docking scores based on structural data describing a ligand-protein complex (i.e., the atom types and their partial charges, distance between the atoms, and the amino acid types). Such approaches may not represent the optimal choice to improve docking prediction performances when dealing with recently identified, or not yet thoroughly studied, therapeutic targets
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