Philippe Schwaller

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Publications associées (32)

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Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Philippe Schwaller, Jeff Guo

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

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

Marta Anna Brucka, Anna Kelmanson, Magdalena Lederbauer, Matthew Ryan Hart, Huixuan Guo, Philippe Schwaller, Geemi Piyathma Wellawatte

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

Chemical education in digital chemistry

Philippe Schwaller

In this digital age where machine learning has won the Nobel Prizes in both Physics and Chemistry, it is ever more important to give chemistry students an educational advantage that will enable them to use the tools of artificial intelligence and machine l ...

CELL PRESS2024

A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes

Raffaella Buonsanti, Philippe Schwaller, Ludovic Emile Frédy Zaza, Bojana Rankovic

The ability to precisely design colloidal nanocrystals (NCs) has far-reaching implications in optoelectronics, catalysis, biomedicine, and beyond. Achieving such control is generally based on a trial-and-error approach. Data-driven synthesis holds promise ...

2025

Alchemical analysis of FDA approved drugs

Philippe Schwaller, Daniel Probst

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools desi ...

2023

Beam Enumeration: Probabilistic Explain-Ability for Sample Efficient Self-Conditioned Molecular Design

Philippe Schwaller, Jeff Guo

Generative molecular design has moved from proof-of-concept to real-world applicability, as marked by the surge in very recent papers reporting experimental validation. Key challenges in explainability and sample efficiency present opportunities to enhance ...

International Conference on Learning Representations, ICLR2024

Tango*: constrained synthesis planning using chemically informed value functions

Daniel Paul Armstrong, 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

Augmenting large language models with chemistry tools

Andres Camilo Marulanda Bran, Carlo Baldassari, Philippe Schwaller, Oliver Tobias Schilter

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ...

Nature Portfolio2024

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

Philippe Schwaller

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Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

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

Chemical education in digital chemistry

A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes

Alchemical analysis of FDA approved drugs

Beam Enumeration: Probabilistic Explain-Ability for Sample Efficient Self-Conditioned Molecular Design

Tango*: constrained synthesis planning using chemically informed value functions

Augmenting large language models with chemistry tools

Highly parallel optimisation of chemical reactions through automation and machine intelligence

Comment on "Molecular hypergraph neural networks"

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

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