[HTML][HTML] Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design
The expansion of the chemical space to tangible libraries containing billions of
synthesizable molecules opens exciting opportunities for drug discovery, but also …
synthesizable molecules opens exciting opportunities for drug discovery, but also …
Cheminformatics and artificial intelligence for accelerating agrochemical discovery
Y Djoumbou-Feunang, J Wilmot, J Kinney… - Frontiers in …, 2023 - frontiersin.org
The global cost-benefit analysis of pesticide use during the last 30 years has been
characterized by a significant increase during the period from 1990 to 2007 followed by a …
characterized by a significant increase during the period from 1990 to 2007 followed by a …
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision …
ADMETlab 3.0 is the second updated version of the web server that provides a
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
We investigate the potential of graph neural networks for transfer learning and improving
molecular property prediction on sparse and expensive to acquire high-fidelity data by …
molecular property prediction on sparse and expensive to acquire high-fidelity data by …
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …
molecular properties. However, because of the complexity of chemical space, such models …
Will we ever be able to accurately predict solubility?
Accurate prediction of thermodynamic solubility by machine learning remains a challenge.
Recent models often display good performances, but their reliability may be deceiving when …
Recent models often display good performances, but their reliability may be deceiving when …
Geometric deep learning for molecular property predictions with chemical accuracy across chemical space
MR Dobbelaere, I Lengyel, CV Stevens… - Journal of …, 2024 - Springer
Chemical engineers heavily rely on precise knowledge of physicochemical properties to
model chemical processes. Despite the growing popularity of deep learning, it is only rarely …
model chemical processes. Despite the growing popularity of deep learning, it is only rarely …
Towards out-of-distribution generalizable predictions of chemical kinetics properties
Machine Learning (ML) techniques have found applications in estimating chemical kinetics
properties. With the accumulated drug molecules identified through" AI4drug discovery", the …
properties. With the accumulated drug molecules identified through" AI4drug discovery", the …
Unleashing the potential of cell painting assays for compound activities and hazards prediction
F Odje, D Meijer, E Von Coburg… - Frontiers in …, 2024 - frontiersin.org
The cell painting (CP) assay has emerged as a potent imaging-based high-throughput
phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico …
phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico …
Developing machine learning models for accurate prediction of radiative efficiency of greenhouse gases
Abstract Background Greenhouse gases (GHGs), particularly halocarbons, contribute
significantly to the radiative forcing of climate change due to their long lifetime. Accurate …
significantly to the radiative forcing of climate change due to their long lifetime. Accurate …