Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

Kumar, Ankur; Kumar, Vinay; Ojha, Probir Kumar; Roy, Kunal

doi:10.1016/j.yrtph.2024.105572

Cited by 7 publications

(1 citation statement)

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“…RASAR seamlessly combines the principles of read-across and QSAR. ,− Thus, in the construction of c-RASAR models, it becomes essential to take into account aspects related to similarity throughout the model development process. ,, The c-RASAR approach relies on these measures of similarity and error as fundamental descriptors. ,, In this investigation, to implement c-RASAR modeling, we utilized the RASAR-Desc-Calc tool v3.0.2 (available from ) to compute RASAR descriptors, taking the imbalanced training set and test set matrices as inputs. The read-across approach guided the selection of the optimum similarity measure.…”

Section: Methodsmentioning

confidence: 99%

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction

Kumar,

Banerjee,

Roy

2024

J. Chem. Inf. Model.

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The intricate nature of the blood−brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure−activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both readacross and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https:// github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

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