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:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
By C\'esar Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Mart\'in Carballo-Pacheco
arXiv:2607. 01105v1 Announce Type: new Abstract: 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 make it).
By Miruna Cretu, John Bradshaw, Patricia Suriana, Saeed Saremi, Omar Mahmood, Kirill Shmilovich, Kangway Chuang, Vishnu Sresht, Colin Grambow
arXiv:2602. 04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice.
By Hyeonah Kim, Minsu Kim, Celine Roget, Dionessa Biton, Louis Vaillancourt, Yves V. Brun, Yoshua Bengio, Alex Hernandez-Garcia
arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.
By Sarina Kopf, Cristina Nevado, Philippe Schwaller
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
By Zhu Wang, Jiangyu Chen, Yingjun Shang, Yuhui Yao, Laiao Lu, Tianfan Fu, Na Zou
arXiv:2606. 23745v1 Announce Type: cross Abstract: We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands.
By Zygimantas Jocys, Zhanxing Zhu, Henriette M. G. Willems, Katayoun Farrahi
arXiv:2606. 00008v1 Announce Type: new Abstract: Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes.
By Jia Zhang, Tengfei Ma, Tianle Li, Daojian Zeng, Xieping Gao, Xiangxiang Zeng
arXiv:2510. 24380v2 Announce Type: replace Abstract: Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts.
By Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley, Darren J Hsu, Kushal S Shah, Elias Stehle, Jingrong Zhang, Izhar Wallach
arXiv:2501. 12434v3 Announce Type: replace-cross Abstract: Motivation: Retrosynthesis plays a crucial role in organic synthesis and drug discovery, focusing on identifying a set of reactants capable of synthesizing a target product molecule.
By Jiaxi Zhuang, Yu Zhang, Ying Qian, Aimin Zhou
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:2411. 06608v3 Announce Type: replace Abstract: We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design.
By Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen