Molecular dynamics and structural-biology modeling.
17 papers in this topic (ICML 2026).
Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Joey Bose +1
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann…
Julong Yang, Wen Huang, Junhui Chen, Jian Peng
Recent advances in diffusion models show promise for Structure-Based Drug Design (SBDD), which aims to generate 3D ligand molecules that bind tightly to specific protein targets.…
Winfried Ripken, Michael Plainer, Gregor Lied, Thorben Frank +4
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a…
Pingzhi Li, Hongxuan Li, Zirui Liu, Xingcheng Lin +1
Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain…
Mujie Lin, Yutian Liu, Yudi Guo, Yanzhen Hou +4
Generating long-horizon molecular dynamics (MD) is difficult due to error accumulation in time-domain autoregressive models, which causes drift, and fixed step-size constraints on…
Ziyang Yu, Yi He, Wenbing Huang, Wen Yan +1
Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress,…
Weilong Chen, Bojun Zhao, Jan Eckwert, Julija Zavadlav
Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood…
Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian +1
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials…
Yunyang Li, Lin Huang, Luojia Xia, Wenhe Zhang +1
Generative models for 3D molecular conformations must respect Euclidean symmetries and concentrate probability mass on thermodynamically favorable, mechanically stable structures.…
Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M Blau +1
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in…
Parth Verma, Parv Pratap Singh, Vipul Garg, Ishita Thakre +2
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new…
Haokai Hong, Wanyu Lin, KC Tan
Large-scale molecular dynamics simulations are essential in understanding chemical and biological processes, necessitating the accurate and efficient modeling of interatomic…
Zhiran Hou, Tinghuai Ma, Huan Rong, Li Jia +3
Predicting molecular properties from three-dimensional structures is fundamentally hindered by limited labeled data. While researchers have adapted self-supervised pre-training…
Yitian Wang, Fanmeng Wang, Angxiao Yue, Wentao Guo +2
Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties. This task is challenging because a…
Kevin Han, Haolin Cong, Bowen Deng, Amir Barati Farimani
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications.…
Arthur Kosmala, Stephan Günnemann, Meng Gao, Brandon M. Wood
Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system…
Jiyeon Kim, Byungju Lee, Won-Yong Shin
Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).