Search Results for author: Philippe Schwaller

Found 13 papers, 6 papers with code

Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design

no code implementations10 May 2023 Jeff Guo, Philippe Schwaller

Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark.

Data Augmentation Drug Discovery

ChemCrow: Augmenting large-language models with chemistry tools

no code implementations11 Apr 2023 Andres M Bran, Sam Cox, Andrew D White, Philippe Schwaller

Large-language models (LLMs) have recently shown strong performance in tasks across domains, but struggle with chemistry-related problems.

Drug Discovery

Identification of Enzymatic Active Sites with Unsupervised Language Modeling

no code implementations NeurIPS Workshop AI4Scien 2021 Loïc Kwate Dassi, Matteo Manica, Daniel Probst, Philippe Schwaller, Yves Gaetan Nana Teukam, Teodoro Laino

Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.

Language Modelling

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design

no code implementations6 May 2021 Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee

Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning.

BIG-bench Machine Learning Chemical Reaction Prediction

Unassisted Noise Reduction of Chemical Reaction Data Sets

1 code implementation2 Feb 2021 Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino

Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones).

Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery

no code implementations10 Feb 2020 Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Ozkirimli

Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.

Drug Discovery

Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy

no code implementations17 Oct 2019 Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino

We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.

"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

1 code implementation13 Nov 2017 Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, Teodoro Laino

With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy.


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