Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in-silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.
Drug Release Modeling using Physics-Informed Neural Networks
Division of Applied Mathematics, Brown University（应用数学系，布朗大学）；School of Engineering, Brown University（工程学院，布朗大学）；Institute for Biology, Engineering and Medicine, Brown University（生物、工程与医学研究所，布朗大学）
Differential equations play a pivotal role in modern world ranging from science, engineering, ecology, economics and finance where these can be used to model many physical systems and processes. In this paper, we study two mathematical models of a drug assimilation in the human system using Physics Informed Neural Networks (PINNs). In the first model, we consider the case of single dose of drug in the human system and in the second case, we consider the course of this drug taken at regular intervals. We have used the compartment diagram to model these cases. The resulting differential equation…
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
Academy of Mathematics and Systems Science（数学与系统科学研究院）；Chinese Academy of Sciences（中国科学院）；Institute for AI Industry Research (AIR)（智能产业研究院（AIR））；Tsinghua University（清华大学）
Novartis（诺华）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Jet Propulsion Laboratory, Caltech（加州理工学院喷气推进实验室）；Temple University（天普大学）
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance wh…
An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining
College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院）；Faculty of Health Sciences, University of Macau（澳门大学健康科学学院）；Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University（浙江省智能药物发现与开发重点实验室，浙江大学金华研究院）；Shanghai Innovation Institute（上海创新研究院）
A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
Modelling Engineering Risk and Complexity, Scuola Superiore Meridionale（工程风险与复杂性建模，南方高级学校）；Institute of Science and Technology for Energy and Sustainable Mobility, Consiglio Nazionale delle Ricerche（能源与可持续移动科技研究所，国家研究理事会）
MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学化学工程系）；Faculty of Industrial Engineering, KU Leuven（鲁文大学工业工程学院）；University of Surrey（萨里大学）
Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Microsoft Research AI4Science（微软研究院AI4Science）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）
Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments. Preferable therapeutic treatments of OUD rely on the advances in understanding the neurobiological mechanism of opioid dependence. Proteins including mu, delta, kappa, nociceptin, and zet…
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
University of California Berkeley（加州大学伯克利分校）；DAMO Academy（达摩院）；University of Oxford（牛津大学）；California Institute of Technology（加州理工学院）；University of Toronto（多伦多大学）；National Research Council Canada（加拿大国家研究委员会）
A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Politecnico di Milano（米兰理工大学）；Department of Physical and Chemical Sciences, University of L’Aquila（拉奎拉大学物理与化学科学系）；Istituto Italiano di Tecnologia（意大利理工学院）；E4 Computer Engineering（E4计算机工程公司）；CINECA；Dompé farmaceutici S.p.A.（Dompé制药股份公司）
Protein-ligand interactions (PLIs) are fundamental to biochemical research and their identification is crucial for estimating biophysical and biochemical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation …
