ICML 2026 (International Conference on Machine Learning) is one of the three leading machine-learning venues. This page curates 315 AI-for-biomedicine papers from ICML 2026 — from protein design, genomic foundation models, and molecular docking to clinical LLMs and medical imaging. 27 are spotlights; 195 ship code.
Data source: BioTender-max/icml2026-ai-bio (CC0-1.0) · updated July 10, 2026 · about the data
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…
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…
Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang +4
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via…
Zhitao He, Haolin Yang, Zeyu Qin, Yi R. Fung
While Large Language Models (LLMs) have achieved remarkable success in dyadic (one-on-one) instruction, they face significant challenges in One-to-Many alignment, such as clinical…
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.…
Arnav Shah, Junzhe Li, Parsa Idehpour, Adibvafa Fallahpour +4
Genomic foundation models have the potential to decode DNA syntax, yet face a fundamental tradeoff. Standard subword tokenizers fragment biologically meaningful motifs such as…
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…
Minghan Li, Fengji Li, Yilin Tao, Yue Deng
Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly…
Kieran Didi, Sarah Alamdari, Alex Xijie Lu, Bruce James Wittmann +4
Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered…
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…
Tingting Chen, Beibei Lin, Zifeng Yuan, Qiran Zou +4
Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effective inference requires not…
Amrith Lotlikar, Ian Christopher Tanoh, Praful K. Vasireddy, Andrew Lanpouthakoun +4
Multi-compartment Hodgkin–Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical…
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…
SUBBA REDDY OOTA, Vijay Rowtula, Satya Sai Srinath Namburi GNVV, Khushbu Pahwa +4
Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which…
Dian Ding, Liren Dong, Yu Lu, Juntao Zhou +4
Gastrointestinal (GI) motility assessment via bowel sounds (BS) offers a non-invasive alternative to resource-intensive clinical standards. However, the diagnostic utility of BS…
Yizhuo Lu, Changde Du, Qingyu Shi, Hang Chen +4
Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior…
Haowei Xu, Yixin Chen, Wanyi Fu, Hongbin Han +1
Neuromodulation can be viewed as closed-loop control of high-dimensional spatiotemporal fields on irregular 3D morphologies, coupling membrane electrophysiology with ionic…
Lingyuan Meng, KE LIANG, Hao Li, Meng Liu +4
Multi-modal brain network analysis aims to predict neuropsychiatric status from functional connectomes with heterogeneous phenotypes. However, most existing methods treat…
Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu +1
We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using…
Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoid duplicates and by models…
Chenggang Chen, Zhiyu Yang, Xiaoqin Wang
Deep neural networks currently provide the leading quantitative models of neural responses in sensory systems. However, these networks remain implausible as models of sensory…
Lina Zhang, Tonmoy Monsoor, Peizheng Li, Jiarui Cui +4
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in general video understanding, their capacity to interpret involuntary, and…
Zongzhe Xu, Zitao Shuai, Eideen Mozaffari, Ravi Shankar Aysola +2
We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical…
Marie Brockschmidt, Maresa Schröder, Stefan Feuerriegel
Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death. Unlike standard tabular data,…
Hanqun Cao, Aastha Pal, Sophia Tang, Yinuo Zhang +3
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
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…
Zipeng Sun, Can Chen, Ye Yuan, Haolun Wu +3
We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics, DNA, and materials…