Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening,...

Full description

Saved in:
Bibliographic Details
Published in:Future medicinal chemistry 2022-02, Vol.14 (4), p.245-270
Main Authors: Kumar, Sethu Arun, Ananda Kumar, Thirumoorthy Durai, Beeraka, Narasimha M, Pujar, Gurubasavaraj Veeranna, Singh, Manisha, Narayana Akshatha, Handattu Sankara, Bhagyalalitha, Meduri
Format: Article
Language:English
Subjects:
Artificial Intelligence
blackbox models
Blood-Brain Barrier - metabolism
Decision Making
Deep Learning
Drug Delivery Systems
Drug Discovery
Drug Industry
Drug Repositioning
high-quality data acquisition
Humans
Machine Learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
ISSN:1756-8919
1756-8927
DOI:10.4155/fmc-2021-0243