AIMC Topic: Small Molecule Libraries

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Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.

Proceedings of the National Academy of Sciences of the United States of America
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training dat...

Maximizing the integration of virtual and experimental screening in hit discovery.

Expert opinion on drug discovery
INTRODUCTION: Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approac...

Artificial intelligence in virtual screening: Models versus experiments.

Drug discovery today
A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensiv...

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.

International journal of molecular sciences
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponent...

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries.

Cells
Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule gene...

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

Nature protocols
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking onl...

Don't Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models.

Molecules (Basel, Switzerland)
Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches tha...

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

International journal of molecular sciences
In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays a...