Molecular design, drug screening, and modeling drug–target interactions.
47 papers in this topic (ICML 2026).
Vincent Guan, Lazar Atanackovic, Kirill Neklyudov
The population dynamics of molecules, cells, and organisms are governed by a number of unknown internal and external forces. In the last decade, population dynamics have…
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.…
Nicholas Gao, Till Grutschus, Frank Noe, Stephan Günnemann
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient…
Friedrich Hastedt, Dongda Zhang, Antonio Del rio chanona
Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or…
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…
Kexin Zhang, Weichen Qin, Yue Teng, Jiale Yu +4
Agent-centered scientific discovery is turning scientific models into always-on computational infrastructure. In this paradigm, AI agents coordinate tools, interpret feedback, and…
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…
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary Ward Ulissi
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive bias. However, as models scale to larger…
Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley, Darren J. Hsu +4
Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts. However, their large size presents a challenge for…
Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger, Kjell Jorner +1
Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g.,…
Lisa Schneckenreiter, Sohvi Luukkonen, Lukas Friedrich, Daniel Kuhn +1
Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this…
Zhuoran Li, Xu Sun, Chang Wen Chen, Wanyu Lin
Controllable molecule generation is crucial for diverse scientific applications, such as drug discovery and materials design. While large language models (LLMs) show great…
Luca Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge A. Campos-Gonzalez-Angulo +4
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions.…
Sihan Wang, Wenjie Du, Yang Wang
In the AI4Chemistry scenario, utilizing heterogeneous data at different fidelity levels is a common and core issue. High-fidelity data is accurate but scarce, while low-fidelity…
Yingheng Wang, Tao Yu, Shufeng Kong, Francesco Ricci +2
Structured scientific spectra encode rich physical information while obeying hard constraints, such as conservation laws and characteristic spectral geometry. Accurate prediction…
Eike Eberhard, Luca Thiede, Abdulrahman Aldossary, Andreas Burger +4
Machine-learned (ML) XC functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently…
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu +1
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with…
Stefan Pranger, Bernhard Ramsauer, Oliver T. Hofmann, Bettina Könighofer
Scanning tunneling microscopy (STM) enables precise manipulation of individual atoms and molecules, offering a pathway to constructing nanoscale assemblies with rich quantum…
Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang +2
Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for…
Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer +3
Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.…
Jiahao Chen, Letian Gao, Yanhao Zhu, Wenbiao zhou +3
Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty…
Yongqi Jin, Yecheng Wang, Jun-jie Wang, Rong Zhu +2
Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning…
Sung Moon Ko, Jaewan Lee, Sumin Lee, Soorin Yim +1
Geometrical interpretations of deep learning models offer insightful perspectives into their underlying mathematical structures. In this work, we introduce a novel approach that…
Haoran liu, Xiaoli Lin, Jing Hu, Yu Zou +1
Polypharmacology provides a powerful strategy for treating complex diseases, but identifying molecules that simultaneously satisfy coupled constraints across multiple biological…
Hongyu Wang, Weijian Liu, Hongtao Xu, Yan Wang +3
Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy…
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…
Hwanhee Kim, Seungyeon Choi, Sanghyun Park
Generative Flow Networks (GFlowNets) have emerged as a powerful framework for molecular generation, sampling diverse candidates proportionally to a reward function. However, the…
Robin Winter, Julian Cremer, Djork-Arné Clevert
Virtual screening of billion-scale molecular libraries based on 3D shape similarity remains computationally prohibitive, requiring expensive conformational sampling and alignment,…
Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan +1
Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spectral ambiguity and the vast…
Chenguang Wang, Zihan Zhou, LEI BAI, Tianshu Yu
Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries…
Zhixiang Cheng, Hongxin Xiang, Mingquan Liu, Tengfei Ma +4
Precise property prediction of organic materials is pivotal for next-generation electronic and energy devices. In density functional theory (DFT), the electron density (ED) serves…
ZIYU XU, zijian zhang, Liang Wang, Zhiyuan Liu +3
When reliable target structures are unavailable at scale or phenotypes arise from dysregulated pathways, transcriptomic perturbations provide a system-level functional readout for…
Zeyu Wang, Xin Zheng, Yao Lu, Shanqing Yu +2
Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain.…
Xinyi Li, Sai Wang, Yutian Lin, Yu Wu
Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely…
Liao Chang, Luotian Yuan, Yiping Ke, Ying Wei
Multi-step retrosynthesis planning is a fundamental challenge in organic chemistry, defined by its enormous search space. Existing methods typically formulate it as a Markov…
Wenhan Gao, Jingxiang Qu, Yi Liu
Diffusion models typically operate in fixed-dimensional metric spaces, whereas 3D geometric molecular data vary in dimensionality because molecules differ in size (number of…
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…
Xuanning Hu, Hao Tuo, Jinglong Ji, Anchen Li +2
Structure-based drug design (SBDD) can be effectively realized through an iterative refinement via the Design-Make-Test-Analyze (DMTA) cycle, which is a common workflow used by…
Kiet Bennema ten Brinke, Koen Minartz, Vlado Menkovski
Simulating trajectories of dynamical systems is a fundamental problem in a wide range of fields such as molecular dynamics, biochemistry, and pedestrian dynamics. Machine learning…
Miruna Cretu, John Bradshaw, Patricia Suriana, Saeed Saremi +4
We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to…
Mohammad Haddadnia, Yuvan Chali, Abhilash Jayaraj, Constance Kraay +3
Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a…
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…
Haorui Wang, Parshin Shojaee, Kazem Meidani, Kunyang Sun +4
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many…
Zekai Chen, Xunkai Li, Sirui Zhang, Henan Sun +4
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…
Yaoyu Chen, Xiaoli Lin, Ziyi Gong, Jun Pang
Efficiently designing high-quality molecules targeting disease-relevant targets is a critical challenge. Most existing methods can capture pairwise amino acid relations,…
Yuchen Hua, Xingang Peng, Jianzhu Ma, Muhan Zhang
Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent…
Bogdan Zagribelnyy, Ivan Ilin, Maksim Kuznetsov, Nikita Bondarev +4
Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evaluation of retrosynthesis performance…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).