AIMC Topic: Quantitative Structure-Activity Relationship

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Applying machine learning techniques for ADME-Tox prediction: a review.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, eff...

Predicting purification process fit of monoclonal antibodies using machine learning.

mAbs
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purifi...

Computational prediction of mutagenicity through comprehensive cell painting analysis.

Mutagenesis
The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine le...

Prediction of bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) for per- and polyfluoroalkyl substances (PFASs) using Read-Across and q-RASPR.

The Science of the total environment
Per- and polyfluoroalkyl substances (PFASs) contamination poses an environmental concern due to their ability to bioaccumulate in aquatic species and adversely impact human health. Experimental bioconcentration factor (log BCF) data of freshwater fis...

Developing a quantitative structure-property relationships (QSPR) model using Caco-2 cell bioavailability indicators (BA) to predict the BA of phytochemicals.

Journal of the science of food and agriculture
BACKGROUND: The present study aimed to measure bioavailability (BA) indicators, including epithelial barrier function, apparent permeability (P) and efflux ratio, of 84 types of phytochemicals using Caco-2 cell and to develop predictive model systems...

Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells.

Environmental toxicology and chemistry
In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by genera...

Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.

Journal of chemical information and modeling
Drug discovery is a lengthy and resource-intensive process that requires innovative computational techniques to expedite the transition from laboratory research to life-saving medications. Here, we introduce Qsarna, a comprehensive online platform th...

SMILES Token Additivity Model with Interpretability and Generalizability for Fuel Property Predictions.

Journal of chemical information and modeling
Deep learning models for the quantitative structure-property relationship (QSPR) have traditionally encountered challenges related to limited interpretability and generalizability. In this study, we present the simplified molecular input line entry s...

First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.

Molecular diversity
In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 i...

In silico design strategies for tubulin inhibitors for the development of anticancer therapies.

Expert opinion on drug discovery
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...