Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Machine Learning with Applications, Год журнала: 2024, Номер 17, С. 100576 - 100576

Опубликована: Июль 24, 2024

Язык: Английский

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Continuous flow synthesis enabling reaction discovery

Chemical Science, Год журнала: 2024, Номер 15(13), С. 4618 - 4630

Опубликована: Янв. 1, 2024

This article defines the role that continuous flow chemistry can have in new reaction discovery, thereby creating molecular assembly opportunities beyond our current capabilities. Most notably focus is based upon photochemical, electrochemical and temperature sensitive processes where methods machine assisted processing significant impact on chemical reactivity patterns. These platforms are ideally placed to exploit future innovation data acquisition, feed-back control through artificial intelligence (AI) learning (ML) techniques.

Язык: Английский

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Machine learning-guided strategies for reaction conditions design and optimization

Beilstein Journal of Organic Chemistry, Год журнала: 2024, Номер 20, С. 2476 - 2492

Опубликована: Окт. 4, 2024

This review surveys the recent advances and challenges in predicting optimizing reaction conditions using machine learning techniques. The paper emphasizes importance of acquiring processing large diverse datasets chemical reactions, use both global local models to guide design synthetic processes. Global exploit information from comprehensive databases suggest general for new while fine-tune specific parameters a given family improve yield selectivity. also identifies current limitations opportunities this field, such as data quality availability, integration high-throughput experimentation. demonstrates how combination engineering, science, ML algorithms can enhance efficiency effectiveness design, enable novel discoveries chemistry.

Язык: Английский

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Challenges and opportunities for computer-aided molecular and process design approaches in advancing sustainable pharmaceutical manufacturing

Current Opinion in Chemical Engineering, Год журнала: 2025, Номер 47, С. 101073 - 101073

Опубликована: Янв. 11, 2025

Язык: Английский

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An automatic end-to-end chemical synthesis development platform powered by large language models

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Ноя. 23, 2024

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development synthetic reactions. In this work, we leveraged power GPT-4 build an LLM-based reaction framework (LLM-RDF) handle fundamental tasks involved throughout chemical synthesis development. LLM-RDF comprises six specialized 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 with as backend was built allow chemist users interact automated experimental platforms analyze results via natural language, thus, eliminating need for coding skills ensuring accessibility all chemists. We demonstrated capabilities in guiding end-to-end process copper/TEMPO catalyzed aerobic alcohol oxidation aldehyde reaction, literature search information extraction, substrate scope condition screening, kinetics study, optimization, scale-up product purification. Furthermore, LLM-RDF's broader applicability versability validated on various three distinct reactions (SNAr photoredox C-C cross-coupling heterogeneous photoelectrochemical reaction). rise offers new advancing synthesis. Here, authors developed copilot design

Язык: Английский

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In Silico Prediction of Pharmaceutical Degradation Pathways: A Benchmarking Study Using the Software Program Zeneth

Organic Process Research & Development, Год журнала: 2024, Номер 28(3), С. 674 - 692

Опубликована: Фев. 19, 2024

Zeneth, a software application for the prediction of chemical degradation small organic molecules, incorporates knowledge base rules to predict pathways. In addition, contains property predictors that modulate predicted likelihood given product. this study, C–H bond dissociation energy (C–H BDE) predictor, which has been integrated into software, was utilized. To determine software's predictive capabilities [using its (2020.1.0 KB)], experimentally derived profiles 25 drug substances were compared Zeneth predictions. These from forced studies, including accelerated and long-term stability aligned with International Council Harmonisation (ICH) guidelines. two case studies highlighting how data can be utilized confirm experimental or assist identification unknown products have presented. The specificity results evaluated; transformation types often not observed identified, an assessment causes is sensitivity study group also evaluated using historic (2012.2.0 KB), enabling improved over period; comparison demonstrated 40% increase in sensitivity. This ongoing expansion optimization silico tools continues result improvements capability ability impart insight space aid pharmaceutical development.

Язык: Английский

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

Опубликована: Май 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.

Язык: Английский

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Bayesian data-driven models for pharmaceutical process development

Current Opinion in Chemical Engineering, Год журнала: 2024, Номер 45, С. 101034 - 101034

Опубликована: Июнь 5, 2024

Язык: Английский

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Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

Beilstein Journal of Organic Chemistry, Год журнала: 2025, Номер 21, С. 10 - 38

Опубликована: Янв. 6, 2025

The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring high-dimensional parametric space. Historically, optimization has been performed by manual experimentation guided human intuition and through design experiments where reaction variables are modified one at time to find specific outcome. Recently, paradigm change in enabled advances lab automation introduction machine learning algorithms. Therein, multiple can be synchronously optimized obtain conditions, requiring shorter minimal intervention. Herein, we review currently used state-of-the-art high-throughput automated platforms algorithms drive reactions, highlighting limitations future opportunities this new field research.

Язык: Английский

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Design and Optimization of a shared synthetic route for multiple active pharmaceutical ingredients through combined Computer Aided Retrosynthesis and flow chemistry

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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