Editorial to Special Issue—“Structure-Activity Relationships (SAR) of Natural Products”

Sippl, Wolfgang; Ntie‐Kang, Fidele

doi:10.3390/molecules26020250

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…The study combines information from several machine learning (ML) algorithms to identify correct L, PT candidates, and combinations of two (popularly called as structure–activity-relationship or SAR or quantitative structure–activity-relationship or QSAR ) at the outset of a drug design [ 5 ]. In SAR, from the structural features of the compound, its biological activities are predicted.…”

Section: Introductionmentioning

confidence: 99%

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Chattopadhyay,

Do,

Flower

et al. 2023

Med Biol Eng Comput

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Extracting “high ranking” or “prime protein targets” (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations — ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository (http://DUD-E.decoys.org) are matched against 1040 ligands using “forward modeling” machine learning for initial “data mining and modeling” (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called “reverse modeling” (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Chattopadhyay,

Do,

Flower

et al. 2023

Med Biol Eng Comput

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Structural activity comparison of abutilon indicum and synthetic NSAIDs by using spectroscopy and chromatography techniques

Paul

Gowda

Roopa

et al. 2022

Materials Today: Proceedings

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Editorial to Special Issue—“Structure-Activity Relationships (SAR) of Natural Products”

Abstract: The topic of structure-activity-relationships (SAR) has recently drawn a lot of attention, and there is increasing interest in natural products (NPs) as a “source of inspiration” for the discovery of new lead compounds [...]

Cited by 2 publications

References 28 publications

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Structural activity comparison of abutilon indicum and synthetic NSAIDs by using spectroscopy and chromatography techniques

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