Academic Articles & Research Papers

Structure-activity relationship of GSK-3β inhibitors: insight into drug design for Alzheimer's disease.

Bioactive Peptides in Probiotic Fermented Foods: Structure-Activity Relationships and Cardiometabolic Protective Effects.

Amidoxime functionalized chitosan for uranium sequestration in vivo

Amidoxime functionalized chitosan (AC) was recommended as a chelator for uranium sequestration in vivo in this study, and the structure-activity relationship was also explored. Compared with ZnNa3-DTPA, which was a commercial uranium mobili...

Research progress in high thermal conductivity of silicon carbide matrix composites reinforced with fibers

As one kind of advanced high temperature structural and functional materials, it is necessary for fiber reinforced silicon carbide matrix composites (SiC CMCs) in the field of thermal management (TM) to combine the efficient heat transfer a...

High Dimensional QSAR Study of Mild Steel Corrosion Inhibition in acidic medium by Furan Derivatives

The inhibition of mild steel corrosion in 1 M HCl by 17 furan derivatives was investigated experimentally using potentiodynamic polarization measurements. The furan derivatives inhibit the mild steel corrosion. The experimental inhibition e...

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. Here the authors present a deep neural network model - DeepPROTACs - for predicting the degradation capacity of a proposed PROTAC molecule....

ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...

Machine learning methods in chemoinformatics

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics ...

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like...