arXiv Machine Learning

APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries

arXiv:2510. 24380v2 Announce Type: replace Abstract: Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts.

arXiv Machine Learning
Jun 26

Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space

arXiv:2606. 26657v1 Announce Type: new Abstract: 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 prominent example.

By Mohammad Haddadnia, Yuvan Chali, Abhilash Jayaraj, Constance Kraay, Joana Reis, Felix Strieth-Kalthoff, Haribabu Arthanari
arXiv Machine Learning
Jun 16

Generative Molecular Design with Steerable and Granular Synthesizability Control

arXiv:2505. 08774v2 Announce Type: replace-cross Abstract: Designing molecules that are both property-optimal and readily synthesizable is a central challenge in drug discovery.

By Jeff Guo, V\'ictor Sabanza-Gil, Olha Semenenko, Oleksii Hrabovskyi, Mykola Protopopov, Anna Kapeliukha, Oleksandr Mosia, Sofiia Hatych, Diana Alieksieieva, Tom Nelis, Patrick Molliet, Helena Sol\'e-\`Avila, Valentas Olikauskas, Nina Aregger, Irina Morozova, Joseph Schmidt, Zlatko Jon\v{c}ev, Olga Tarkhanova, Petro Borysko, Jerome Waser, Bruno Correia, Jeremy Luterbacher, Philippe Schwaller
arXiv Machine Learning
Jul 16

HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration

arXiv:2607. 13155v1 Announce Type: new Abstract: Generative molecular models can support early drug discovery by proposing new candidate compounds de novo.

By Daria A. Ryabchenko (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Pavel Gurevich (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Shamil Kadyrov (Ligand Pro, Moscow, Russia), Daria Frolova (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Kseniia Fedisheva (Ligand Pro, Moscow, Russia), Sergei A. Nikolenko (Ligand Pro, Moscow, Russia), Alexander Shapeev (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Marina A. Pak (Ligand Pro, Moscow, Russia)
arXiv AI
6d ago

A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization

arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.

By Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha
arXiv AI
Aug 10

Strategy-first synthesis planning for complex natural products

arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.

By Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Th\'eo A. Neukomm, Tadd\"aus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Cl\'ement Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller
arXiv Machine Learning
Jun 5

An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining

arXiv:2606. 05198v1 Announce Type: cross Abstract: Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucleic acid structures remains challenging.

By Shi Li (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Xujun Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Mingquan Liu (Faculty of Health Sciences, University of Macau, Macau SAR, China), Hui Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Shuoying Jia (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Yu Kang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Tingjun Hou (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China), Peichen Pan (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China)