Artificial Intelligence for Autonomous Molecular Design: A Perspective
Abstract
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 technologymore »
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1829517
- Alternate Identifier(s):
- OSTI ID: 1833355
- Report Number(s):
- PNNL-SA-159775
Journal ID: ISSN 1420-3049; MOLEFW; PII: molecules26226761
- Grant/Contract Number:
- NVBL; AC05-76RL01830
- Resource Type:
- Published Article
- Journal Name:
- Molecules
- Additional Journal Information:
- Journal Name: Molecules Journal Volume: 26 Journal Issue: 22; Journal ID: ISSN 1420-3049
- Publisher:
- MDPI AG
- Country of Publication:
- Switzerland
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; autonomous workflow; therapeutic design; computer aided drug discovery; computational modeling and simulations; quantum mechanics and quantum computing; artificial intelligence; machine learning; deep learning; machine reasoning and causal inference and causal reasoning
Citation Formats
Joshi, Rajendra P., and Kumar, Neeraj. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Switzerland: N. p., 2021.
Web. doi:10.3390/molecules26226761.
Joshi, Rajendra P., & Kumar, Neeraj. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Switzerland. https://doi.org/10.3390/molecules26226761
Joshi, Rajendra P., and Kumar, Neeraj. Tue .
"Artificial Intelligence for Autonomous Molecular Design: A Perspective". Switzerland. https://doi.org/10.3390/molecules26226761.
@article{osti_1829517,
title = {Artificial Intelligence for Autonomous Molecular Design: A Perspective},
author = {Joshi, Rajendra P. and Kumar, Neeraj},
abstractNote = {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.},
doi = {10.3390/molecules26226761},
journal = {Molecules},
number = 22,
volume = 26,
place = {Switzerland},
year = {Tue Nov 09 00:00:00 EST 2021},
month = {Tue Nov 09 00:00:00 EST 2021}
}
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