Automatic design of novel potential 3CL^pro and PL^pro inhibitors

2021/01/28 by Timothy Atkinson, Saeed Saremi, Atkinson, Timothy +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Artificial Intelligence (cs.AI) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.2101.11890

openalex publication_date 2021/01/28 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28

Abstract

With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we propose the general molecule optimization framework, Molecular Neural Assay Search (MONAS), consisting of three components: a property predictor which identifies molecules with specific desirable properties, an energy model which approximates the statistical similarity of a given molecule to known training molecules, and a molecule search method. In this work, these components are instantiated with graph neural networks (GNNs), Deep Energy Estimator Networks (DEEN) and Monte Carlo tree search (MCTS), respectively. This implementation is used to identify 120K molecules (out of 40-million explored) which the GNN determined to be likely SARS-CoV-1 inhibitors, and, at the same time, are statistically close to the dataset used to train the GNN.

Citations

• Auto-Encoding Variational Bayes
• Geometric Deep Learning: Going beyond Euclidean data
• Towards Sparse Hierarchical Graph Classifiers
• Grammars and reinforcement learning for molecule optimization
• Graph Neural Networks: A Review of Methods and Applications
• Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• Hierarchical Graph Pooling with Structure Learning
• Inhibition of the Main Protease 3CL-pro of the Coronavirus Disease 19 via Structure-Based Ligand Design and Molecular Modeling

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• Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery
• Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19