Year: 2023
Type: article
Abstract: Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.
Source: Communications Biology
Authors: Li Shen, Hongsong Feng, Yuchi Qiu, Guo-Wei Wei
Institution: Michigan State University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,690
Funders: National Science Foundation, Foundation for the National Institutes of Health, National Aeronautics and Space Administration, Bristol-Myers Squibb, Pfizer, Michigan State University, Nuclear Safety and Security Commission, National Institutes of Health, Pfizer Foundation, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases
Awards: AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies, G2333693723, G314050322, G4604530488, Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets, G5117234996, G5344812377, Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data, G6065440738, III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data, G754965114, G9000099788, G9000294604, G945659648, G965062884
OpenAlex ID: W4377094475