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Gouvernement du Canada / Government of Canada

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Conversational drug editing using retrieval and domain feedback

Par Conseil national de recherches du Canada

Lienhttps://iclr.cc/virtual/2024/poster/17419
AuteurRechercher : ; Rechercher : ; Rechercher : ; Rechercher : ; Rechercher : ; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0002-7663-2421; Rechercher :
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
FormatTexte, Affiche
Conférence12th International Conference on Learning Representations (ICLR 2024), May 7-11, 2024, Vienna, Austria
Description physique1 p.
Résumé
Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversational LLMs on chemical reactions and retrosynthesis. While drug editing, a critical task in the drug discovery pipeline, remains largely unexplored. To bridge this gap, we propose ChatDrug, a framework to facilitate the systematic investigation of drug editing using LLMs. ChatDrug jointly leverages a prompt module, a retrieval and domain feedback module, and a conversation module to streamline effective drug editing. We empirically show that ChatDrug reaches the best performance on all 39 drug editing tasks, encompassing small molecules, peptides, and proteins. We further demonstrate, through 10 case studies, that ChatDrug can successfully identify the key substructures for manipulation, generating diverse and valid suggestions for drug editing. Promisingly, we also show that ChatDrug can offer insightful explanations from a domain-specific perspective, enhancing interpretability and enabling informed decision-making.
Date de publication
Maison d’éditionInternational Conference on Learning Representations, ICLR
Open Review
Dans
  • ICLR 2024: The 12th International Conference on Learning Representations (7 mai 2024).
Autre format
Langueanglais
Publications évaluées par des pairsNon
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Identificateur de l’enregistrement0eb0340c-c79c-4be0-892f-691b0bce0285
Enregistrement créé2024-10-25
Enregistrement modifié2025-07-16
Date de modification :