LDDM (Large Drug Discovery Model) is introduced, a generative framework that supports a range of drug discovery tasks, including constrained and unconstrained docking, fragment linking and growing, and de novo design, and a programmable design algorithm that enables accurate design of synthetically accessible compounds...
Ilia Igashov, Arne Schneuing, Adrian W. Dobbelstein et al.· bioRxiv· 0 citations
Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop.
Joshua W. Sin, David Ming Segura, Bojana Ranković et al.· 0 citations
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. It is also a profoundly creati...
Daniel P. Armstrong, X. Nguyen, Octavian Susanu et al.· 0 citations
A framework that systematically standardizes and integrates multiple reaction datasets into a high-quality, unique-structure-per-entity dataset, coupled with active learning to strategically expand chemical space is introduced, establishing a blueprint for robust machine learning in synthetic chemistry.
Paulo Neves, Bo Hao, Santeri Aikonen et al.· Nature Computational Science· 1 citation
This work introduces MAELLE (MechAnistic Edit fLow-matching on eLectron rEarrangements), which instead models reactions as discrete flow matching over electron occupation vectors and naturally recovers mechanistic trajectories that align with known chemistry and can predict side products of a reaction.
X. Nguyen, Octavian Susanu, Daniel P. Armstrong et al.· 1 citation
This work introduces Sample Efficient Generative Optimization (SEGO), a framework for Bayesian optimization on adaptively generated molecules, and attains state-of-the-art performance on the practical molecular optimization (PMO) benchmark using only one tenth of the oracle calls consumed by other methods.
S. Kopf, Cristina Nevado, P. Schwaller· 0 citations
Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extended these architectures to chemistry. However, domain-adaptive pre-training often causes models to overfit to chemical syntax, catastrophically forgetting their foundatio...
David Ming Segura, Jeremy Goumaz, Joshua W. Sin et al.· 1 citation
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