AIMC Topic: Molecular Structure
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HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.
Molecular informatics
Nov 23, 2020
Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely use...
H-RACS: a handy tool to rank anti-cancer synergistic drugs.
Aging
Nov 10, 2020
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug s...
Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.
Molecular pharmaceutics
Nov 5, 2020
Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alter...
Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence.
ACS combinatorial science
Nov 4, 2020
Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to "immortal cancer" in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marke...
Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks.
Journal of chemical information and modeling
Oct 14, 2020
We develop a convolutional neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential represented as a 4D tensor. This method effectively bypasse...
A Turing Test for Molecular Generators.
Journal of medicinal chemistry
Oct 12, 2020
Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecu...
Activity prediction of aminoquinoline drugs based on deep learning.
Biotechnology and applied biochemistry
Oct 11, 2020
The results of the traditional prediction method for the activity of aminoquinoline drugs are inaccurate, so the prediction method for the activity of aminoquinoline drugs based on the deep learning is designed. The molecular holographic distance vec...
Efficient molecular encoders for virtual screening.
Drug discovery today. Technologies
Oct 4, 2020
Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges...
Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.
Nature communications
Sep 25, 2020
Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions i...
A new diarylhexane and two new diarylpropanols from the roots of .
Natural product research
Sep 16, 2020
A new diarylhexane, kneglobularone B () and two new diarylpropanols, kneglobularols A - B () along with seven known compounds () were isolated and characterized from the roots of It is the first time to find arylpropyl quinone () and isoflavone () i...
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