Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization.
Journal:
Journal of chemical information and modeling
Published Date:
Oct 26, 2020
Abstract
Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.
Authors
Keywords
Acetylcholinesterase
Antiviral Agents
Artificial Intelligence
Betacoronavirus
Cholinesterase Inhibitors
Coronavirus 3C Proteases
Coronavirus Infections
COVID-19
Cysteine Endopeptidases
Drug Design
Enzyme Inhibitors
Humans
Influenza A virus
Molecular Docking Simulation
Neural Networks, Computer
Neuraminidase
Pandemics
Pneumonia, Viral
SARS-CoV-2
Small Molecule Libraries
Viral Nonstructural Proteins