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Assessing drug-target affinity is a critical step in the drug discovery and development process, but to obtain such data experimentally is both time consuming and expensive. For this reason, computational methods for predicting binding…

Machine Learning · Computer Science 2022-09-15 Elizaveta Vinogradova , Karina Pats , Ferdinand Molnár , Siamac Fazli

The field of drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins, especially when such proteins directly influence disease progression. However, estimating binding…

Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. Binding affinity, which characterizes the strength of biomolecular interactions, is essential for tackling diverse challenges…

The concepts and methods of Systems Biology are being extended to neuropharmacology, to test and design drugs against neurological and psychiatric disorders. Computational modeling by integrating compartmental neural modeling technique and…

Neurons and Cognition · Quantitative Biology 2007-05-23 Peter Erdi , Tamas Kiss , Janos Toth , Balazs Ujfalussy , Laszlo Zalanyi

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to…

In the present work we compare reliability of several most widely used reduced detailed chemical kinetic schemes for hydrogen-air and hydrogen-oxygen combustible mixtures. The validation of the schemes includes detailed analysis of 0D and…

Fluid Dynamics · Physics 2013-12-13 M. F. Ivanov , A. D. Kiverin , M. A. Liberman , A. E Smygalina

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive…

Quantitative Methods · Quantitative Biology 2022-05-18 Ziyi Yang , Zhaofeng Ye , Yijia Xiao , Changyu Hsieh , Shengyu Zhang

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component…

Biological Physics · Physics 2022-10-03 Simon Bray , Victor Tänzel , Steffen Wolf

Computational methods in drug repositioning can help to conserve resources. In particular, methods based on biological networks are showing promise. Considering only the network topology and knowledge on drug target genes is not sufficient…

Molecular Networks · Quantitative Biology 2025-04-02 Atte Aalto , La Mi , Diego A. Blanco-Mora , Jorge Goncalves

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

A novel approach to simulate simple protein-ligand systems at large time- and length-scales is to couple Markov state models (MSMs) of molecular kinetics with particle-based reaction-diffusion (RD) simulations, MSM/RD. Currently, MSM/RD…

Chemical Physics · Physics 2021-12-10 Mauricio J. del Razo , Manuel Dibak , Christof Schütte , Frank Noé

Recent developments in enhanced sampling methods showed that it is possible to reconstruct ligand unbinding pathways with spatial and temporal resolution inaccessible to experiments. Ideally, such techniques should provide an atomistic…

Biological Physics · Physics 2019-12-17 Jakub Rydzewski

Structure-based drug design (SBDD) aims to generate 3D ligand molecules that bind to specific protein targets. Existing 3D deep generative models including diffusion models have shown great promise for SBDD. However, it is complex to…

Biomolecules · Quantitative Biology 2024-03-01 Zhilin Huang , Ling Yang , Zaixi Zhang , Xiangxin Zhou , Yu Bao , Xiawu Zheng , Yuwei Yang , Yu Wang , Wenming Yang

Predicting drug-target interaction (DTI) is critical in the drug discovery process. Despite remarkable advances in recent DTI models through the integration of representations from diverse drug and target encoders, such models often…

Quantitative Methods · Quantitative Biology 2025-09-30 Zhaohan Meng , Zaiqiao Meng , Ke Yuan , Iadh Ounis

The long-term efficacy of targeted therapeutics for cancer treatment can be significantly limited by the type of therapy and development of drug resistance, inter alia. Experimental studies indicate that the factors enhancing acquisition of…

Tissues and Organs · Quantitative Biology 2021-02-16 Joseph Malinzi , Kevin Bosire Basita , Sara Padidar , Henry A. Adeola

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to…

Along with recent progress in structural biology and genome biology, structural dynamics of molecular systems including nucleic acids has attracted attention in the context of gene regulation. Structure-function relationship is an important…

Biomolecules · Quantitative Biology 2021-11-23 Takeru Kameda , Akinori Awazu , Yuichi Togashi

Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics…

Chemical Physics · Physics 2023-08-14 Dhiman Ray , Michele Parrinello

The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on the underlying…

Applications · Statistics 2023-02-10 Yiding Zhang , Zhixing Xu , Hui Quan , Ji Lin

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach…

Computational Physics · Physics 2021-04-28 Shunzhou Wan , Robert C. Sinclair , Peter V. Coveney