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Structure-Activity Relationship

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Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Molecules (Basel, Switzerland)
The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, includ...

Exploration of the nanomedicine-design space with high-throughput screening and machine learning.

Nature biomedical engineering
Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining struct...

Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Computational biology and chemistry
BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline m...

SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

Chemical biology & drug design
GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machi...

In silico prediction of chemical reproductive toxicity using machine learning.

Journal of applied toxicology : JAT
Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strate...

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities.

mAbs
Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. ...

Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

Pharmaceutical research
PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the i...

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Proceedings of the National Academy of Sciences of the United States of America
Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these l...

G-Networks to Predict the Outcome of Sensing of Toxicity.

Sensors (Basel, Switzerland)
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through ...