Publications de Philippe Schwaller

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Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Reaction additives are critical in dictating the outcomes of chemical processes making their effective screening vital for research. Conventional high-throughput experimentation tools can screen multiple reaction components rapidly. However, they are prohi ...

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

We review the physics opportunities of the Future Circular Collider, covering its e(+)e(-), pp, ep and heavy ion programmes. We describe the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions, ...

The efficiency of ternary organic solar cells relies on the spontaneous establishment of a nanostructured network of donor and acceptor phases during film formation. A fundamental understanding of phase composition and arrangement and correlations to photo ...

The chemisorption energy of reactants on a catalyst surface, Eads, is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents signi ...

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often ...

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it ...

Language modeling has seen impressive progress over the last few years. The invention of the Transformer architecture sparked a revolution in many fields of machine learning, including breakthroughs in chemistry and biology. In this chapter, we explore how ...

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

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 ...

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronic ...

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 ...

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the ...

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 ...

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 ...

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 ...

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to ext ...

Philippe Schwaller

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Machine learning-aided generative molecular design

Bayesian optimisation for additive screening and yield improvements - beyond one-hot encoding

Bayesian Optimization for Chemical Reactions

FCC Physics Opportunities: Future Circular Collider Conceptual Design Report Volume 1

Insights into photovoltaic properties of ternary organic solar cells from phase diagrams

Interpreting chemisorption strength with AutoML-based feature deletion experiments

Balancing computational chemistry's potential with its environmental impact

Accelerated chemical science with AI

Transformers and Large Language Models for Chemistry and Drug Discovery

Artificial intelligence for natural product drug discovery

Molecular hypergraph neural networks

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Targeting protein-ligand neosurfaces with a generalizable deep learning tool

Leveraging large language models for predictive chemistry

SELFIES and the future of molecular string representations

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

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

A multi-modal transformer for predicting global minimum adsorption energy

FSscore: A Personalized Machine Learning-Based Synthetic Feasibility Score