SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

Abstract

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

“…SperoPredictor, an integrated ML and molecular docking‐based drug repurposing framework. (A) drug‐disease data collection and preparation from various sources, (B) shows the training, testing, and validation of ML algorithms, and (C) finally shows the validation of predictions 198 . ML, machine learning.…”

“…12,13 With the advent of next generation sequencing (NGS) technology [14][15][16][17][18] such as single cell RNA [19][20][21][22] sequencing the ability to model explore the complex phenomenon such as cancer heterogeneity, viral cancers, drug resistance and etiologies have become easily accessible. Additionally, the unprecedented progress in computational power 23 and computational strategies of machine learning (ML) 4,24,25 and deep learning (DL) [26][27][28][29][30] in combination with NGS single-cell RNA technologies have started to yield better results. Development of databases related to cancer, drugs, and gene expression profiles [31][32][33] have proved key components to the implementation of ML and DL in drug discovery and drug repurposing for cancer.…”

Revolutionizing drug development: harnessing the potential of organ-on-chip technology for disease modeling and drug discovery

“…The study identified altered IFN-1, CXCL10, and IL-6 biomarkers. This platform provides a valuable tool to investigate the underlying mechanisms of asthma and viral infection ( Nawroth et al, 2020 ; Ahmed et al, 2022e ). Longlong et al discovered a novel class of immune response-stimulating RNAs while investigating host genes linked to influenza infection in human lung epithelial cells using siRNAs.…”

Navigating with chemometrics and machine learning in chemistry

“…AI-learning modelled with molecular descriptors, functional-class fingerprints (FCFPs), chemical fingerprints, and physico-chemical properties like partition coefficients could screen and identify drugs for treating coronavirus patients. It is revealed that drug repurposing for COVID-19 primarily utilized three types of algorithms viz, network-based (Ge et al 2021 ), expression-based (Pham et al 2021 ) and integrated docking simulations (Ahmed et al 2022 ). Sibilio et al ( 2021 ) examined three different network-based algorithms to identify potential drug molecules using transcriptomic data from the WBCs of COVID infected patients.…”