ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback Article Swipe
Shengchao Liu
,
Jiongxiao Wang
,
Yijin Yang
,
Chengpeng Wang
,
Ling Liu
,
Hongyu Guo
,
Chaowei Xiao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18090
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18090
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 reaction 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 (ReDF) module, and a conversation module to streamline effective drug editing. We empirically show that ChatDrug reaches the best performance on 33 out of 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 (e.g., the molecule functional groups, peptide motifs, and protein structures) 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. This research sheds light on the potential of ChatGPT and conversational LLMs for drug editing. It paves the way for a more efficient and collaborative drug discovery pipeline, contributing to the advancement of pharmaceutical research and development.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.18090
- https://arxiv.org/pdf/2305.18090
- OA Status
- green
- Cited By
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378771157
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378771157Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.18090Digital Object Identifier
- Title
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ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain FeedbackWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-29Full publication date if available
- Authors
-
Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei XiaoList of authors in order
- Landing page
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https://arxiv.org/abs/2305.18090Publisher landing page
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https://arxiv.org/pdf/2305.18090Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2305.18090Direct OA link when available
- Concepts
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Computer science, Pipeline (software), Drug discovery, Domain (mathematical analysis), Conversation, Interpretability, Data science, Artificial intelligence, Bioinformatics, Psychology, Communication, Biology, Mathematical analysis, Mathematics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 11, 2023: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.peptides, | 113 |
| abstract_inverted_index.pipeline, | 48, 198 |
| abstract_inverted_index.potential | 177 |
| abstract_inverted_index.proteins. | 115 |
| abstract_inverted_index.retrieval | 78 |
| abstract_inverted_index.discovery. | 20 |
| abstract_inverted_index.facilitate | 62 |
| abstract_inverted_index.functional | 134 |
| abstract_inverted_index.generating | 143 |
| abstract_inverted_index.insightful | 159 |
| abstract_inverted_index.molecules, | 112 |
| abstract_inverted_index.remarkable | 13 |
| abstract_inverted_index.streamline | 89 |
| abstract_inverted_index.systematic | 64 |
| abstract_inverted_index.advancement | 202 |
| abstract_inverted_index.empirically | 94 |
| abstract_inverted_index.performance | 101 |
| abstract_inverted_index.structures) | 140 |
| abstract_inverted_index.suggestions | 147 |
| abstract_inverted_index.unexplored. | 51 |
| abstract_inverted_index.Promisingly, | 151 |
| abstract_inverted_index.advancements | 1 |
| abstract_inverted_index.capabilities | 29 |
| abstract_inverted_index.contributing | 199 |
| abstract_inverted_index.conversation | 86 |
| abstract_inverted_index.demonstrate, | 118 |
| abstract_inverted_index.demonstrated | 12 |
| abstract_inverted_index.development. | 207 |
| abstract_inverted_index.encompassing | 110 |
| abstract_inverted_index.explanations | 160 |
| abstract_inverted_index.perspective, | 164 |
| abstract_inverted_index.successfully | 126 |
| abstract_inverted_index.collaborative | 195 |
| abstract_inverted_index.investigating | 27 |
| abstract_inverted_index.investigation | 65 |
| abstract_inverted_index.manipulation, | 142 |
| abstract_inverted_index.substructures | 130 |
| abstract_inverted_index.conversational | 3, 31, 181 |
| abstract_inverted_index.pharmaceutical | 204 |
| abstract_inverted_index.domain-specific | 163 |
| abstract_inverted_index.retrosynthesis. | 37 |
| abstract_inverted_index.decision-making. | 170 |
| abstract_inverted_index.interpretability | 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |