Publications de Philippe Schwaller

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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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

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 ...

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

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