Covalent small-molecule ligands have re-emerged as powerful tools in drug discovery, offering prolonged target engagement, enhanced potency, and the ability to modulate proteins once considered undruggable. However, the rational design and virtual screening (VS) of covalent ligands remain challenging. Many docking tools cannot accurately model the energetics of covalent bond formation, often requiring more rigorous quantum mechanical (QM) or semi-empirical QM calculations for reliable predictions. Despite these limitations, the computational landscape is rapidly evolving. An increasing number of open-source, commercial, and web-based platforms now support binding mode exploration, lead optimization, and structure-based VS of covalent ligands. Alongside traditional approaches, new artificial intelligence (AI) and machine learning (ML) tools are assisting in prioritizing candidate molecules. This review introduces the fundamental principles and mechanisms of covalent inhibition, then provides a comprehensive overview of computational tools including covalent docking, warhead placement algorithms, and pharmacophore modeling, supporting early-stage drug discovery and chemical biology. Case studies highlight practical applications. We also cover curated databases of covalent binders and experimental 3D protein-ligand complexes, plus tools for assessing nucleophilic residue reactivity, all essential for robust covalent modeling. Finally, we briefly address risks associated with covalent chemistry. While progress is notable, further advances are needed. Nonetheless, today's covalent docking and AI-driven tools already make a meaningful impact by enabling rational design, generating new ideas, refining hypotheses, and expanding the boundaries of druggability.

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

SUMMARY: Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer. AVAILABILITY AND IMPLEMENTATION: FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identification of small molecules that could be interesting starting points for drug discovery or to investigate a biological system as in chemical biology endeavours is both time consuming and costly. In silico approaches that assist the design of quality compound collections or help to prioritize molecules before synthesis or purchase are therefore valuable. Here quality refers to the selection of molecules that pass one or several selected filters that can be tuned by the users according to the project and the stage of the project. These filters can involve prediction of physicochemical properties, search for toxicophores or other unwanted chemical groups. RESULTS: FAF-Drugs4 is a novel version of our online server dedicated to the preparation and annotation of compound collections. The tool is now faster and several parameters have been optimized. In addition, a new service referred to as FAF-QED, an implementation of the quantitative estimate of drug-likeness method, is now available. AVAILABILITY AND IMPLEMENTATION: The server is available at http://fafdrugs4.mti.univ-paris-diderot.fr. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

The vital role of Protein-Protein Interactions (PPI) for Life makes them the subject of a growing number of drug discovery projects. Yet, the specific properties of PPI (often described as flat, large and hydrophobic) require a dramatic paradigm shift in our way to design the small compounds meant to modulate them with therapeutic perspectives. To this end, successful inhibitors of PPI targets (iPPI) may be used to discover what singular properties make this type of inhibitors capable of binding to such intricate surfaces. Among the properties from which lessons could be learnt, the 3D characteristics of iPPI have been pinpointed as essential. Understanding the putative shape profile of iPPI could help the design of a new generation of inhibitors. In an attempt to identify 3D characteristics, we have collected the bioactive conformations of 84 orthosteric iPPI and compared them to those of 1282 inhibitors of conventional targets (e.g enzymes) collectively from different databases (2P2I [ 1 ], PDBbind[ 2 ], PDB). Because the known heavier and more hydrophobic character of iPPI could conceal other characteristics, we have imposed that none of the identified descriptors could correlate with the hydrophobicity or the size of the compound. Four 3D characteristics were highlighted (Figure 1 ). They describe either the shape of the compounds (globularity) or the 3D distributions of the hydrophobic and hydrophilic interacting regions of the compounds (IW4, EDmin3, CW2: VolSurf descriptors [ 3 ]). More specifically the most essential property revealed in the analysis (EDmin3) illustrates how iPPI manage to bind to the hydrophobic patches often present at the core of PPI targets. The newly identified properties were further confirmed as characteristic to iPPI using the data of much larger datasets including our iPPI-DB[ 4 ], eDrugs3D[ 5 ] and a representative subset of the bindingDB[ 6 ]. Bioactive conformation of compound 1MQ as cocrystallized with Mdm2 (pdb code 4JVE ) . The compound is represented as transparent molecular surface and molecular sticks. The value of highlighted descriptors are : EDmin3 = -3.18 kcal/mol (represented by the green molecular field calculated using Moe 2012.10 at the levels of energy equal to -2.4 kcal/mol using a dry probe), IW4 = 4.13 (represented by the pink molecular field calculated using Moe 2012.10 at the levels of energy equal to -5.5 kcal/mol using a water probe), glob = 0.20 (represented by the molecular surface), and CW2 = 1.90 (represented by the proportion of pink surface over the full molecular surface). Identifying low-molecular-weight iPPI is known to be a difficult task. This has usually been translated into designing compounds with higher size, aromaticity, and hydrophobicity. Yet, lessons are being learnt from iPPI bioactive conformations in an attempt to circumvent this trend. During this analysis, we demonstrated that the capacity to bind a protein-protein interface partially rely on the combination of several structural and electrostatic features including the globularity and the distribution of hydrophilic regions but most importantly of hydrophobic interacting regions. More distinctively, iPPI seem to be characterized by a significantly higher efficiency to bind the hydrophobic patches often present at PPI interfaces. The absence of correlation of this type of property with the hydrophobicity and the size of the compounds could open new ways to design iPPI with improved ligand and lipophilic efficiencies and may allow the scientific community to anticipate an era of more drug-like iPPI.

Abstract Summary:The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters. Availability: The FAF-Drugs2 server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/FAF-Drugs/. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.

BACKGROUND: Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. RESULTS: This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. CONCLUSION: We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.

BACKGROUND: Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization. RESULTS: The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection. CONCLUSION: The open source AMMOS program can be helpful in a broad range of in silico drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.

BACKGROUND: The number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most protein-ligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites. RESULTS: Here we present an efficient multiple conformation rigid-body docking approach, MS-DOCK, which is based on the program DOCK. This approach can be used as the first step of a multi-stage docking/scoring protocol. First, we developed and validated the Multiconf-DOCK tool that generates several conformers per input ligand. Then, each generated conformer (bioactives and 37970 decoys) was docked rigidly using DOCK6 with our optimized protocol into seven different receptor-binding sites. MS-DOCK was able to significantly reduce the size of the initial input library for all seven targets, thereby facilitating subsequent more CPU demanding flexible docking procedures. CONCLUSION: MS-DOCK can be easily used for the generation of multi-conformer libraries and for shape-based filtering within a multi-step structure-based screening protocol in order to shorten computation times.