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De novo ligand design is a fundamental task that seeks to generate protein or molecule candidates that can effectively dock with protein receptors and achieve strong binding affinity entirely from scratch. It holds paramount significance…

Machine Learning · Computer Science 2025-10-13 Zekai Chen , Xunkai Li , Sirui Zhang , Henan Sun , Jia Li , Zhenjun Li , Bing Zhou , Rong-Hua Li , Guoren Wang

Due to inherent complexity active transport presents a landmark hurdle for oral absorption properties prediction. We present a novel approach carrier-mediated drug absorption parameters calculation based on entirely different paradigm than…

Quantitative Methods · Quantitative Biology 2008-10-16 P. O. Fedichev , T. V. Kolesnikova , A. A. Vinnik

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical…

Machine Learning · Computer Science 2022-11-08 Ryien Hosseini , Filippo Simini , Austin Clyde , Arvind Ramanathan

Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized…

Biomolecules · Quantitative Biology 2024-07-24 Yang Zhang , Zhewei Wei , Ye Yuan , Chongxuan Li , Wenbing Huang

Molecular docking is critical to structure-based virtual screening, yet the throughput of such workflows is limited by the expensive optimization of scoring functions involved in most docking algorithms. We explore how machine learning can…

Biomolecules · Quantitative Biology 2024-09-04 Bowen Jing , Tommi Jaakkola , Bonnie Berger

Advancements in AI for science unlocks capabilities for critical drug discovery tasks such as protein-ligand binding affinity prediction. However, current models overfit to existing oversimplified datasets that does not represent naturally…

Machine Learning · Computer Science 2025-12-02 Ming-Hsiu Wu , Ziqian Xie , Shuiwang Ji , Degui Zhi

Beta-turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine-learning techniques such as SVM, neural networks, and K-NN have achieved good results for beta-turn pre-diction,…

Biomolecules · Quantitative Biology 2018-08-14 Chao Fang , Yi Shang , Dong Xu

Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has…

Biomolecules · Quantitative Biology 2021-09-29 Leonardo V. Castorina , Rokas Petrenas , Kartic Subr , Christopher W. Wood

Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a…

Machine Learning · Computer Science 2026-02-18 Anjie Qiao , Zhen Wang , Yaliang Li , Jiahua Rao , Yuedong Yang

Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accuracy at present, it is…

Biomolecules · Quantitative Biology 2021-05-12 Yeji Wang , Shuo Wu , Yanwen Duan , Yong Huang

Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem…

Transcription factors (TFs) are macromolecules that bind to \textit{cis}-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety…

Computer Vision and Pattern Recognition · Computer Science 2016-11-18 Hamid Reza Hassanzadeh , May D. Wang

Protein interactions and assembly formation are fundamental to most biological processes. Predicting the assembly structure from constituent proteins -- referred to as the protein docking task -- is thus a crucial step in protein design…

Machine Learning · Computer Science 2023-10-11 Vignesh Ram Somnath , Pier Giuseppe Sessa , Maria Rodriguez Martinez , Andreas Krause

This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and…

Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure-property relationships from experimental…

Machine Learning · Computer Science 2021-02-22 Simon Axelrod , Rafael Gomez-Bombarelli

Drug discovery through virtual screening (VS) has become a popular strategy for identifying hits against protein targets. Alongside VS, molecular design further expands accessible chemical space. Together, these approaches have the…

Biomolecules · Quantitative Biology 2025-10-15 Shanzhuo Zhang , Xianbin Ye , Donglong He , Yueyang Huang , Xiaonan Zhang , Xiaomin Fang

Protein-ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Tasks…

Biomolecules · Quantitative Biology 2020-08-11 Arnab Bhadra , Kalidas Y

Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the…

Quantum Physics · Physics 2026-04-27 Mathieu Garrigues , Victor Onofre , Wesley Coelho , S. Acheche

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of $10^8$ molecules), so too…

Quantitative Methods · Quantitative Biology 2021-05-11 David E. Graff , Eugene I. Shakhnovich , Connor W. Coley

We present a highly efficient molecular dynamics scheme for calculating the concentration profile of dopants implanted in group-IV alloy, and III-V zinc blende structure materials. Our program incorporates methods for reducing computational…

Materials Science · Physics 2009-10-31 Keith M. Beardmore , Niels Gronbech-Jensen