Cluster analysis in prediction of biological activity and molecular structure relationship of dipeptidyl peptidase-4 inhibitors for the type two diabetes mellitus treatment

Syarofina, Sarah; Bustamam, Alhadi; Yanuar, Arry; Sarwinda, Devvi; Hermansyah, Oky

doi:10.1063/5.0024215

Cited by 2 publications

(1 citation statement)

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“…An example of k -means clustering is found in the 2019 conference paper by Syarofina et al, in which the authors utilize k -means clustering, along with a variety of clustering evaluators, to create a model capable of discovering molecules that inhibit dipeptidyl peptidase-4 (DPP-IV). 92 DPP-IV is a significant target for the treatment of type 2 diabetes mellitus, and the discovery of potential inhibitors for DPP-IV is an important drug development goal. The model discussed here was trained on a set of 100 known DPP-IV inhibitors prior to subsequent cluster evaluation.…”

Section: Unsupervised Learningmentioning

confidence: 99%

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

Lindley,

Lu,

Shukla

2023

ACS Appl. Bio Mater.

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Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

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Section: Unsupervised Learningmentioning

confidence: 99%