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Cryptic pockets are protein cavities that remain hidden in resolved apo structures and generally require the presence of a co-crystallized ligand to become visible. Finding new cryptic pockets is crucial for structure-based drug discovery to identify new ways of modulating protein activity and thus expand the druggable space. We present here a new method and associated web application leveraging mixed-solvent molecular dynamics (MD) simulations using benzene as a hydrophobic probe to detect cryptic pockets. Our all-atom MD-based workflow was systematically tested on 18 different systems and 5 additional kinases and represents the largest validation study of this kind. CrypticScout identifies benzene probe binding hotspots on a protein surface by mapping probe occupancy, residence time, and the benzene occupancy reweighed by the residence time. The method is presented to the scientific community in a web application available via www.playmolecule.org using a distributed computing infrastructure to perform the simulations.

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities, and kinetics for the selected library and was able to predict 8 mM-affinity fragments with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic core and a positively charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a Markov state model (MSM) framework.

Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson's correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. K

Accurately predicting protein–ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson’s correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein–ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational ch...

Motivation Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein–ligand complexes with the intention of mimicking a chemist’s intuition at manually placing atoms when designing a new compound. We show that these models can generate spatial images of ligand chemical properties like occupancy, aromaticity and donor–acceptor matching the protein pocket. Results The predicted fields considerably overlap with those of unseen ligands bound to the target pocket. Maximization of the overlap between the predicted fields and a given ligand on the Astex diverse set recovers the original ligand crystal poses in 70 out of 85 cases within a threshold of 2 Å RMSD. We expect that these models can be used for guiding structure-based drug discovery approaches. Availability and implementation LigVoxel is available as part of the PlayMolecule.org molecular web application suite. Supplementary information Supplementary data are available at Bioinformatics online.

Summary Virtual screening pipelines are one of the most popular used tools in structure-based drug discovery, since they can can reduce both time and cost associated with experimental assays. Recent advances in deep learning methodologies have shown that these outperform classical scoring functions at discriminating binder protein-ligand complexes. Here, we present BindScope, a web application for large-scale active-inactive classification of compounds based on deep convolutional neural networks. Performance is on a pair with current state-of-the-art pipelines. Users can screen on the order of hundreds of compounds at once and interactively visualize the results. Availability and implementation BindScope is available as part of the PlayMolecule.org web application suite. Supplementary information Supplementary data are available at Bioinformatics online.

Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .