EventsMOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
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Published
with-doi10.3390/mol2net-09-14141 (registering DOI)
This submission belongs to the session 01. CHEMBIO.INFO-09: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress München, GR-Chapel Hill, USA, 2023. of the event MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published date
10 Mar, 2023
Academic Editor
author-avatarMOL2NET Team
Citation
Shan He, Toward Artificial Intelligence Era in Drug Discovery and Design, in Proceedings of MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed., 25 December–31 December 2023, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-09-14141
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Toward Artificial Intelligence Era in Drug Discovery and Design

Shan He 1,2
1. Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of the Basque Country UPV/EHU, P.O.Box 644, 48080 Bilbao, Spain.
2. IKERDATA S.L, ZITEK, UPV/EHU, Rectorate Building, n0 6, Leioa, Greater Bilbao, Basque Country, Spain.
Abstract

In the last decades, we have experienced a revolution in data science in terms of the huge amount of data to be analyzed (era of big data) and the availability of high-performance processors. In drug discovery, this scenario is not different: the large volume of data (chemical, biological, etc.) along with the automation of techniques have generated a fertile ground for the use of artificial (or computational) intelligence/Machine Leaning (AI/ML). This powerful tool helped the researchers to achieve several major theoretical and applied breakthroughs. In this mini-review, recent research work of AI/ML in drug discovery and design will be introduced.

Keywords
Machine Leaning
drug discovery
big data
data science
Manuscript
CHEMBIOINFO-09.pdf
Exploring multivalent interaction in biotechnology.
Cover for Machine Learning in Organic Chemistry