Advanced approaches and in silico tools of chemoinformatics in drug designing

Kulshrestha, Shweta; Arora, Tanmay; Sengar, Manisha; Sharma, Navneet; Chawla, Raman; Bajaj, Shereen; Raghav, Pawan Kumar

doi:10.1016/b978-0-12-821748-1.00006-3

Cited by 2 publications

(1 citation statement)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[17,18] In computational toxicology many in silico methods have been developed and applied such as comparative molecular field analysis, molecular docking, [19] molecular dynamics, [20] read across, structural alerts and quantitative structure-activity relationship (QSAR) models [21] with machine learning (ML) and deep learning (DL). [22] Among many in silico models built to predict nanotoxicity, models based on QSAR, and quantitative structure-property relationship (QSPR) are the most popular and used to predict cytotoxicity and physicochemical properties of NPs. Models built on both QSAR and QSPR methods are based on mathematical statistics and ML algorithms to establish relationships between structure, property, and activity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles

Jyakhwo,

Serov,

Dmitrenko

et al. 2023

Small

View full text Add to dashboard Cite

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high‐throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP‐treated cell lines. The model achieves mean cross‐validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best‐performing selectively cytotoxic NPs. As proof‐of‐concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

show abstract