Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware deve...

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Autores principales: Rajendra P. Joshi, Neeraj Kumar
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Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/ee1ad1a37d474017aaf150dc3a3b2d42
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spelling oai:doaj.org-article:ee1ad1a37d474017aaf150dc3a3b2d422021-11-25T18:27:02ZArtificial Intelligence for Autonomous Molecular Design: A Perspective10.3390/molecules262267611420-3049https://doaj.org/article/ee1ad1a37d474017aaf150dc3a3b2d422021-11-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/22/6761https://doaj.org/toc/1420-3049Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.Rajendra P. JoshiNeeraj KumarMDPI AGarticleautonomous workflowtherapeutic designcomputer aided drug discoverycomputational modeling and simulationsquantum mechanics and quantum computingartificial intelligenceOrganic chemistryQD241-441ENMolecules, Vol 26, Iss 6761, p 6761 (2021)
institution DOAJ
collection DOAJ
language EN
topic autonomous workflow
therapeutic design
computer aided drug discovery
computational modeling and simulations
quantum mechanics and quantum computing
artificial intelligence
Organic chemistry
QD241-441
spellingShingle autonomous workflow
therapeutic design
computer aided drug discovery
computational modeling and simulations
quantum mechanics and quantum computing
artificial intelligence
Organic chemistry
QD241-441
Rajendra P. Joshi
Neeraj Kumar
Artificial Intelligence for Autonomous Molecular Design: A Perspective
description Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.
format article
author Rajendra P. Joshi
Neeraj Kumar
author_facet Rajendra P. Joshi
Neeraj Kumar
author_sort Rajendra P. Joshi
title Artificial Intelligence for Autonomous Molecular Design: A Perspective
title_short Artificial Intelligence for Autonomous Molecular Design: A Perspective
title_full Artificial Intelligence for Autonomous Molecular Design: A Perspective
title_fullStr Artificial Intelligence for Autonomous Molecular Design: A Perspective
title_full_unstemmed Artificial Intelligence for Autonomous Molecular Design: A Perspective
title_sort artificial intelligence for autonomous molecular design: a perspective
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/ee1ad1a37d474017aaf150dc3a3b2d42
work_keys_str_mv AT rajendrapjoshi artificialintelligenceforautonomousmoleculardesignaperspective
AT neerajkumar artificialintelligenceforautonomousmoleculardesignaperspective
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