TY - JOUR
T1 - Artificial intelligence in drug discovery
T2 - recent advances and future perspectives
AU - Jiménez-Luna, José
AU - Grisoni, Francesca
AU - Weskamp, Nils
AU - Schneider, Gisbert
PY - 2021/9
Y1 - 2021/9
N2 - Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
AB - Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
KW - artificial intelligence
KW - de novo drug design
KW - Drug discovery
KW - QSAR
KW - synthesis prediction
KW - Artificial Intelligence
KW - Humans
KW - Drug Design
KW - Drug Discovery
KW - Machine Learning
KW - Quantitative Structure-Activity Relationship
UR - http://www.scopus.com/inward/record.url?scp=85103618190&partnerID=8YFLogxK
U2 - 10.1080/17460441.2021.1909567
DO - 10.1080/17460441.2021.1909567
M3 - Article
C2 - 33779453
VL - 16
SP - 949
EP - 959
JO - Expert Opinion on Drug Discovery
JF - Expert Opinion on Drug Discovery
SN - 1746-0441
IS - 9
ER -