scholarly journals AI – New Avenue for Drug Discovery and Optimization

2021 ◽  
pp. 1-9
Author(s):  
Rupesh Dudhe ◽  
Anshu Chaudhary Dudhe ◽  
Rupesh Dudhe ◽  
Suhas N. Sakarkar ◽  
Omji Porwal
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Treatment ◽  
Drug Target ◽  
Similarity Metrics ◽  
Process Selection ◽  
Treatment Protocols ◽  
Optimal Drug ◽  
Chemical Descriptors ◽  
Selection Of

The artificial intelligence (AI) used in drug treatment have to do with matching patients to their predicting drug-target or drug-drug interactions, optimal drug or combination of drugs, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the suitable drug for a patient typically requires the patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The forecast of drug relations often relies on similarity metrics, pretentious that drugs with similar structures or targeted and similar behaviour or may interfere with each other. Deciding the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamics data.

Artificial Intelligence in Drug Treatment

2020 ◽  
Vol 60 (1) ◽  
pp. 353-369 ◽  
Author(s):  
Eden L. Romm ◽  
Igor F. Tsigelny
Keyword(s):  
Artificial Intelligence ◽  
Drug Treatment ◽  
Drug Interactions ◽  
Drug Target ◽  
Process Selection ◽  
Treatment Protocols ◽  
Optimal Drug ◽  
Chemical Descriptors ◽  
Pharmacodynamic Data ◽  
Selection Of

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.

scholarly journals Commentary: Why Pharmaceutical Scientists in Early Drug Discovery Are Critical for Influencing the Design and Selection of Optimal Drug Candidates

2017 ◽  
Vol 19 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Margaret S. Landis ◽  
Shobha Bhattachar ◽  
Mehran Yazdanian ◽  
John Morrison
Keyword(s):  
Drug Discovery ◽  
Drug Candidates ◽  
Optimal Drug ◽  
Early Drug ◽  
Selection Of

scholarly journals Target-based drug discovery for human African trypanosomiasis: selection of molecular target and chemical matter

Parasitology ◽  
2013 ◽  
Vol 141 (1) ◽  
pp. 28-36 ◽  
Author(s):  
IAN H. GILBERT
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Human African Trypanosomiasis ◽  
Target Selection ◽  
African Trypanosomiasis ◽  
Oral Drugs ◽  
Care Needs ◽  
Physiochemical Properties ◽  
Selection Of ◽  
Chemical Matter

SUMMARYTarget-based approaches for human African trypanosomiasis (HAT) and related parasites can be a valuable route for drug discovery for these diseases. However, care needs to be taken in selection of both the actual drug target and the chemical matter that is developed. In this article, potential criteria to aid target selection are described. Then the physiochemical properties of typical oral drugs are discussed and compared to those of known anti-parasitics.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3339-3349 ◽  
Author(s):  
Indrani Bera ◽  
Pavan V. Payghan
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Drug Target ◽  
Structural Information ◽  
Drug Binding ◽  
Structure Based Drug Design ◽  
Drug Designing ◽  
Target Binding ◽  
Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

scholarly journals Recent Advances in Kinase Drug Discovery Part I: The Editors’ Take

2021 ◽  
Vol 22 (14) ◽  
pp. 7560
Author(s):  
Julie A. Tucker ◽  
Mathew P. Martin
Keyword(s):  
Drug Discovery ◽  
Research Articles ◽  
Special Issue ◽  
Recent Advances ◽  
Enzyme Family ◽  
Selection Of

This special issue on Advances in Kinase Drug Discovery provides a selection of research articles and topical reviews covering all aspects of drug discovery targeting the phosphotransferase enzyme family [...]

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