MolData, A Molecular Benchmark for Disease and Target Based Machine Learning

Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge is necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain meaningful information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChMBL offer the screening data of millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological information which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset are collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https:// github.com/Transilico/MolData as well as within the supplementary materials.

MolData, A Molecular Benchmark for Disease and Target Based Machine Learning

Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain meaningful information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChemBL offer the screening data of millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological information which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveil valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://github.com/Transilico/MolData as well as within the supplementary materials.

Docking Molecular analysis of potential Aldosterone antagonists

Background: Aldosterone antagonists (spironolactone, eplerenone) inhibit the action of aldosterone in the collecting duct; as such, these agents cause modest diuresis but inhibit potassium and hydrogen ion secretion. We report first time Potential Aldosterone antagonists by in Silico approach, using AutoDock Vina and AutoDock 4 (or MGL Tool), estimated with Pyrx and AM Dock Software, calculating three different important parameters: Binding Affinity ( kcal/mol), estimated Ki ( in nM units) and Ligand Efficiency ( L.E. in kcal/mol). After a selective analysis of over 1000 drugs, processed with Pyrx (a Virtual Screening software for Computational Drug Discovery) in the Ligand Binding site pocket of the protein ( ID PDB 2OAX Chain A:), we noticed high values of Binding Energy , about -13.55 kcal/mol estimated by AutoDock 4 with AM Dock Software, concluding that it could be an excellent candidate drug, compared to everyone else Aldosterone antagonists. Indeed, from the results of AutoDock Vina and AutoDock 4 ( or AutoDock 4.2 ), implemented with Lamarckian genetic algorithm, LGA, trough AMDock Software, our results of Binding Energy are very similar to the crystallized Spironolactone in PDB 2OAX Chain A protein.

VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.

Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay

Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein–ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from −6 to −11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein–ligand interactions energy (the poorest being −140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.