Publications de Philippe Schwaller

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

ROYAL SOC CHEMISTRY2025

Highly parallel optimisation of chemical reactions through automation and machine intelligence

Philippe Schwaller

We report the development and application of a scalable machine learning (ML) framework (Minerva) for highly parallel multi-objective reaction optimisation with automated high-throughput experimentation (HTE). Minerva demonstrates robust performance with e ...

2025

Lithium-Mediated Ammonia Electrosynthesis over Orderly Arranged Dipoles Regulated Solid-Electrolyte Interphase

Philippe Schwaller, Junwu Chen

American Chemical Society (ACS)2025

Best practices for multi-fidelity Bayesian optimization in materials and molecular research

Jeremy Luterbacher, Philippe Schwaller

Springer Science and Business Media LLC2025

ChemLit-QA: a human evaluated dataset for chemistry RAG tasks

Philippe Schwaller

Retrieval-Augmented Generation (RAG) is a widely used strategy in Large-Language Models (LLMs) to extrapolate beyond the inherent pre-trained knowledge. Hence, RAG is crucial when working in data-sparse fields such as Chemistry. The evaluation of RAG syste ...

IOP Publishing Ltd2025

A multi-modal transformer for predicting global minimum adsorption energy

Philippe Schwaller, Junwu Chen

The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/a ...

NATURE PORTFOLIO2025

Targeting protein-ligand neosurfaces with a generalizable deep learning tool

Sebastian Maerkl, Bruno Emanuel Ferreira De Sousa Correia, Luc Reymond, Sandrine Madeleine Suzanne Georgeon, Philippe Schwaller, Anthony Marchand, Leo Scheller, Arne Schneuing, Stephen Michael Buckley, Martin Pacesa

Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules2, 3, 4-5. Despite recent ad ...

NATURE PORTFOLIO2025

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Philippe Schwaller

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

NATURE PORTFOLIO2025

Tango*: constrained synthesis planning using chemically informed value functions

Philippe Schwaller, Jeff Guo

TANGO* enables starting material constrained synthesis planning using computed molecular similarity to guide retrosynthetic search toward specific starting materials, achieving superior performance to neural network approaches. ...

Royal Society of Chemistry (RSC)2025

A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists

Philippe Schwaller

Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capa ...

Springer Science and Business Media LLC2025