Dataset Information

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

ABSTRACT: It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

SUBMITTER: Prasad K

PROVIDER: S-EPMC8317454 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

Prasad Kartikay K Kumar Vijay V

Current research in pharmacology and drug discovery 20210728

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing ...[more]

PMID: 34870150

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Project description:AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight "structure prediction" (s = 12.40, R2 = 0.9480, p = 0.0051), "artificial intelligence" (s = 5.00, R2 = 0.8096, p = 0.0375), "drug discovery" (s = 1.90, R2 = 0.7987, p = 0.0409), and "molecular dynamics" (s = 2.40, R2 = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that "structure prediction, artificial intelligence, molecular dynamics" (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), "sars-cov-2, covid-19, vaccine design" (RP = 97.8%, DP = 37.5%), and "homology modeling, virtual screening, membrane protein" (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.

Project description:BackgroundGenome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.MethodsTo address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.ResultsThrough this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.ConclusionsIn summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Dataset Information

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

Publications

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

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