Large language models for reticular chemistry

Nature Reviews Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

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A Review of Large Language Models and Autonomous Agents in Chemistry

Chemical Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities these domains their potential to accelerate scientific discovery through automation. We also LLM-based autonomous agents: LLMs with a broader set of interact surrounding environment. These agents perform diverse tasks such paper scraping, interfacing automated laboratories, planning. As are an emerging topic, we extend the scope our beyond chemistry discuss across any domains. covers recent history, current capabilities, design agents, addressing specific challenges, opportunities, future directions chemistry. Key challenges include data quality integration, model interpretability, need for standard benchmarks, while point towards more sophisticated multi-modal enhanced collaboration between experimental methods. Due quick pace this field, repository has been built keep track latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Language: Английский

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Accelerated end-to-end chemical synthesis development with large language models

Published: May 8, 2024

The rapid emergence of large language model (LLM) technology presents significant opportunities to facilitate the development synthetic reactions. In this work, we leveraged power GPT-4 build a multi-agent system handle fundamental tasks involved throughout chemical synthesis process. comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted accomplish designated tasks. A web application was built with as backend allow chemist users interact experimental platforms analyze results via natural language, thus, requiring zero-coding skills easy access for all chemists. We demonstrated on recently developed copper/TEMPO catalyzed aerobic alcohol oxidation aldehyde reaction, LLM copiloted end-to-end reaction process includes: literature search information extraction, substrate scope condition screening, kinetics study, optimization, scale-up product purification. This work showcases trilogy among users, automated reform traditional expert-centric labor-intensive workflow.

Language: Английский

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Quality by digital design to accelerate sustainable medicines development

International Journal of Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown, P. 125625 - 125625

Published: April 1, 2025

Language: Английский

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Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions

Published: Aug. 28, 2024

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet the identification of competent substrates and their synthesis optimization remains challenging. Here, we report an integrated approach combining machine learning (ML) large language models (LLMs) streamline exploration electrochemical reactions. Utilizing batch rapid screening platform, evaluated wide range reactions, initially classifying by reactivity, while LLMs text-mined literature data augment training set. The resulting ML models, one for reactivity prediction other site selectivity, both achieved high accuracy (>90%) enabled virtual set commercially available molecules. To optimize reaction conditions interest upon screening, were prompted generate code iteratively improve yield, lowering barrier scientists access programs, this strategy efficiently identified high-yield eight drug-like substances or intermediates. Notably, benchmarked reliability 10 different LLMs, including llama, Claude, GPT-4, on generating executing codes related based natural prompts given chemists showcase tool-making tool-using capabilities potentials accelerating research across four diverse tasks. In addition, collected experimental benchmark dataset comprising 1071 yields our findings revealed that integrating outperformed using either method alone. We envision combined offers robust generalizable pathway advancing synthetic chemistry

Language: Английский

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Integrating Machine Learning and Large Language Models to Advance Wu Exploration of Electrochemical Reactions

Published: Aug. 28, 2024

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet the identification of competent substrates and their synthesis optimization remains challenging. Here, we report an integrated approach combining machine learning (ML) large language models (LLMs) streamline exploration electrochemical reactions. Utilizing batch rapid screening platform, evaluated wide range reactions, initially classifying by reactivity, while LLMs text-mined literature data augment training set. The resulting ML models, one for reactivity prediction other site selectivity, both achieved high accuracy (>90%) enabled virtual set commercially available molecules. To optimize reaction conditions interest upon screening, were prompted generate code iteratively improve yield, lowering barrier scientists access programs, this strategy efficiently identified high-yield eight drug-like substances or intermediates. Notably, benchmarked reliability 10 different LLMs, including llama, Claude, GPT-4, on generating executing codes related based natural prompts given chemists showcase tool-making tool-using capabilities potentials accelerating research across four diverse tasks. In addition, collected experimental benchmark dataset comprising 1071 yields our findings revealed that integrating outperformed using either method alone. We envision combined offers robust generalizable pathway advancing synthetic chemistry

Language: Английский

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Overview of Applications of Artificial Intelligence (AI) in Diverse Fields

Published: Jan. 1, 2024

Language: Английский

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