Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Molecular Diversity, Journal Year: 2021, Volume and Issue: 25(3), P. 1315 - 1360

Published: April 12, 2021

Drug designing and development is an important area of research for pharmaceutical companies chemical scientists. However, low efficacy, off-target delivery, time consumption, high cost impose a hurdle challenges that impact drug design discovery. Further, complex big data from genomics, proteomics, microarray data, clinical trials also obstacle in the discovery pipeline. Artificial intelligence machine learning technology play crucial role development. In other words, artificial neural networks deep algorithms have modernized area. Machine been implemented several processes such as peptide synthesis, structure-based virtual screening, ligand-based toxicity prediction, monitoring release, pharmacophore modeling, quantitative structure-activity relationship, repositioning, polypharmacology, physiochemical activity. Evidence past strengthens implementation this field. Moreover, novel mining, curation, management techniques provided critical support to recently developed modeling algorithms. summary, advancements provide excellent opportunity rational process, which will eventually mankind. The primary concern associated with consumption production cost. inefficiency, inaccurate target inappropriate dosage are hurdles inhibit process delivery With technology, computer-aided integrating can eliminate traditional referred superset comprising learning, whereas comprises supervised unsupervised reinforcement learning. subset has extensively network, vector machines, classification regression, generative adversarial networks, symbolic meta-learning examples applied process. different areas synthesis molecule design, screening molecular docking, relationship protein misfolding protein-protein interactions, pathway identification polypharmacology. principles active inactive, pre-clinical development, secondary biomarker manufacturing, bioactivity properties, prediction toxicity, mode action.

Language: Английский

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Advancing Drug Discovery via Artificial Intelligence

Trends in Pharmacological Sciences, Journal Year: 2019, Volume and Issue: 40(8), P. 592 - 604

Published: July 15, 2019

Language: Английский

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Computational/in silico methods in drug target and lead prediction

Briefings in Bioinformatics, Journal Year: 2019, Volume and Issue: 21(5), P. 1663 - 1675

Published: July 24, 2019

Abstract Drug-like compounds are most of the time denied approval and use owing to unexpected clinical side effects cross-reactivity observed during trials. These outcomes resulting in significant increase attrition rate centralizes on selected drug targets. targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes network functions. This is generally linked several factors, including incomplete knowledge unpredicted pharmacokinetic expressions upon target interaction off-target effects. A method used identify targets, especially for polygenic diseases, essential constitutes a major bottleneck development with fundamental stage being identification validation interest further downstream processes. Thus, various computational methods have been developed complement experimental approaches discovery. Here, we present an overview tools applied predicting validating drug-like molecules. We provide their advantages compare these effective which likely lead optimal results. also explore sources failure considering challenges opportunities involved. review might guide researchers selecting efficient approach technique discovery process.

Language: Английский

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Molegro Virtual Docker for Docking

Methods in molecular biology, Journal Year: 2019, Volume and Issue: unknown, P. 149 - 167

Published: Jan. 1, 2019

Language: Английский

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Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Frontiers in Artificial Intelligence, Journal Year: 2020, Volume and Issue: 3

Published: Aug. 18, 2020

SARS-COV-2 has roused the scientific community with a call to action combat growing pandemic. At time of this writing, there are yet no novel antiviral agents or approved vaccines available be deployed as frontline defense. Understanding pathobiology COVID-19 could aid scientists in their discovery potent antivirals by elucidating unexplored viral pathways. One method accomplish is leveraging computational methods discover new candidate drugs and silico. In last decade, machine learning-based models, trained on specific biomolecules, have offered both inexpensive rapid implementation for effective therapies. Given target biomolecule, these models capable predicting inhibitor candidates structural-based manner. If enough data presented model, they can search drug vaccine identifying patterns within data. review, we focus recent advances development using artificial intelligence, potential intelligent training therapeutics. To facilitate applications deep learning SARS-COV-2, highlight multiple molecular targets COVID-19, inhibition which may increase patient survival. Moreover, present CoronaDB-AI, dataset compounds, peptides, epitopes discovered either silico vitro that potentially used models. The information datasets provided review train accelerate therapies y.

Language: Английский

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The strategies and techniques of drug discovery from natural products

Pharmacology & Therapeutics, Journal Year: 2020, Volume and Issue: 216, P. 107686 - 107686

Published: Sept. 19, 2020

Language: Английский

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An Updated Review of Computer‐Aided Drug Design and Its Application to COVID‐19

BioMed Research International, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 1, 2021

The recent outbreak of the deadly coronavirus disease 19 (COVID‐19) pandemic poses serious health concerns around world. lack approved drugs or vaccines continues to be a challenge and further necessitates discovery new therapeutic molecules. Computer‐aided drug design has helped expedite development process by minimizing cost time. In this review article, we highlight two important categories computer‐aided (CADD), viz., ligand‐based as well structured‐based discovery. Various molecular modeling techniques involved in structure‐based are docking dynamic simulation, whereas includes pharmacophore modeling, quantitative structure‐activity relationship (QSARs), artificial intelligence (AI). We have briefly discussed significance context COVID‐19 how researchers continue rely on these computational rapid identification promising candidate molecules against various targets implicated pathogenesis severe acute respiratory syndrome 2 (SARS‐CoV‐2). structural elucidation pharmacological preclinical accelerated both design. This article will help clinicians exploit immense potential designing thereby helping management fatal disease.

Language: Английский

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Intelligent Computing: The Latest Advances, Challenges, and Future

Intelligent Computing, Journal Year: 2023, Volume and Issue: 2

Published: Jan. 1, 2023

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed emergence intelligent computing, new computing paradigm that reshaping traditional and promoting digital revolution era big data, artificial intelligence, internet things with theories, architectures, methods, systems, applications. Intelligent has greatly broadened scope extending it from on data to increasingly diverse paradigms such as perceptual cognitive autonomous human–computer fusion intelligence. Intelligence undergone paths different evolution for long time but become intertwined years: not only intelligence oriented also driven. Such cross-fertilization prompted rapid advancement computing. still its infancy, an abundance innovations applications expected occur soon. We present first comprehensive survey literature covering theory fundamentals, technological important applications, challenges, future perspectives. believe this highly timely will provide reference cast valuable insights into academic industrial researchers practitioners.

Language: Английский

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Artificial Intelligence Applied to clinical trials: opportunities and challenges

Health and Technology, Journal Year: 2023, Volume and Issue: 13(2), P. 203 - 213

Published: Feb. 28, 2023

Language: Английский

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The Integration of Artificial Intelligence into Clinical Practice

Applied Biosciences, Journal Year: 2024, Volume and Issue: 3(1), P. 14 - 44

Published: Jan. 1, 2024

The purpose of this literature review is to provide a fundamental synopsis current research pertaining artificial intelligence (AI) within the domain clinical practice. Artificial has revolutionized field medicine and healthcare by providing innovative solutions complex problems. One most important benefits AI in practice its ability investigate extensive volumes data with efficiency precision. This led development various applications that have improved patient outcomes reduced workload professionals. can support doctors making more accurate diagnoses developing personalized treatment plans. Successful examples are outlined for series medical specialties like cardiology, surgery, gastroenterology, pneumology, nephrology, urology, dermatology, orthopedics, neurology, gynecology, ophthalmology, pediatrics, hematology, critically ill patients, as well diagnostic methods. Special reference made legal ethical considerations accuracy, informed consent, privacy issues, security, regulatory framework, product liability, explainability, transparency. Finally, closes appraising use future perspectives. However, it also approach implementation cautiously ensure met.

Language: Английский

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