New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Molecular Docking, and Machine Learning - Part-VI

: Recently different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing great amounts of chemical data obtaining outcomes to find potential drug candidates.

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A combined drug discovery strategy based on machine learning and molecular docking

Chemical Biology & Drug Design ◽

10.1111/cbdd.13494 ◽

2019 ◽

Vol 93 (5) ◽

pp. 685-699 ◽

Cited By ~ 9

Author(s):

Yanmin Zhang ◽

Yuchen Wang ◽

Weineng Zhou ◽

Yuanrong Fan ◽

Junnan Zhao ◽

...

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Drug Discovery

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SAR and Pharmacophore Based Designing of Some Antimalarial and Antiretroviral Agents: An INTERNET Based Drug Design Approach

American Journal of Undergraduate Research ◽

10.33697/ajur.2013.007 ◽

2013 ◽

Vol 11 (3 and 4) ◽

Author(s):

Soumendranath Bhakat

Keyword(s):

Molecular Docking ◽

Drug Discovery ◽

Drug Design ◽

Computational Chemistry ◽

Operating Systems ◽

Docking Studies ◽

Computational Tools ◽

Structure Activity ◽

System Configurations ◽

User Friendly

With the development of computational chemistry and molecular docking studies, Structure Activity Relationship or SAR- and pharmacophore-based drug design have been modified to target based drug discovery using sophisticated computational tools which is not very much user friendly and has got many incompatibility issues with many operating systems (OS) and other system configurations. In this paper SAR and pharmacophore based drug design approaches have been described by the used of free internet based tools which are very much user friendly and can almost compatible with any platform. Some antimalarial. And anti retroviral agents have been designed using pharmacophore study and their drug like properties, toxicity, metabolic sites and other parameters are predicted by the free internet based tools.

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Improving ensemble docking for drug discovery by machine learning

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619200013 ◽

2019 ◽

Vol 18 (03) ◽

pp. 1920001 ◽

Cited By ~ 1

Author(s):

Chung F. Wong

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Drug Discovery ◽

Inexpensive Method ◽

Receptor Flexibility ◽

Ensemble Docking ◽

Ensemble Machine Learning ◽

Multiple Structures

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.

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Faculty Opinions recommendation of Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.739749744.793584168 ◽

2021 ◽

Author(s):

John Lowe

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Medicinal Chemistry ◽

Model Based ◽

Early Drug

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