The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools. (16th September 2021)

• Record Type: Journal Article
• Authors: Bule, Mohammed; Jalalimanesh, Nafiseh; Bayrami, Zahra; Baeeri, Maryam; Abdollahi, Mohammad
• Is Part Of: Chemical biology & drug design. Volume 98:Number 5(2021)
• Page Start: 954
• Page End: 967
• Publication Date: 2021-09-16
• Subjects: artificial intelligence -- deep learning -- machine learning -- neural networks -- QSAR; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13750
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost‐effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost‐effective, and reliable computer‐aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers‐based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand‐based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery. Abstract : An overview of the machine and deep learning, and the applications of artificial neural network in representing the models based on protein‐protein and protein‐ligand interactions. Deep learning approaches in QSAR modelling, QSAR‐related web resources, and challenges and future perspectives of deep learning methods are discussed.