Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review

Abstract

Background: Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increas-ing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered train-ing set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity.Objective: The study aims to review ML-based methods used for VS and applications to Alzheimer’s Disease (AD) drug discovery.Methods: To update the current knowledge on ML for VS, we review thorough backgrounds, explana-tions, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN).Results: All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks – and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for con-ducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation.Conclusion: Different ML techniques are powerful tools for VS, and they have advantages and disad-vantages albeit. ML-based VS can be applied to AD drug development.

Contributing Factors of Neurodegeneration in Alzheimer’s Disease

“…Agents against targets such as BACE1 and APP amyloidosis have proved to be ineffective against AD progression so far. Therefore, further studies in AD pathogenic mechanisms and future utility of artificial intelligence (AI)-based drug discovery tools may aid in developing novel theranostic agents for AD (140,141).…”

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction

“…The prediction that patients with stable MCI will develop AD achieved ROC-AUC of 92.5% [26] , [27] , [28] , [29] . Carpenter and Huang analyzed ML methods (naïve Bayes, kNN, SVM, random forest, and neural networks) for virtual screening (VS) [30] . They presented a workflow for applying ML-based VS to the search for potential therapeutic drugs for AD.…”

Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalize To Unseen Target Classes, And Highlight Important Binding Interactions

“…6 This issue, along with the current deluge of structural data, has prompted the use of machine learning methods in structure-based virtual screening and in the verification of docked structures. [7][8][9] Machine learning has found its way into many scientific domains, and drug discovery is no exception (a review can be found in [10]). Multiple machine learning-based virtual screening methods have been developed.…”