Machine learning, artificial intelligence, and chemistry: How smart algorithms are resha** simulation and the laboratory
D Kuntz, AK Wilson - Pure and Applied Chemistry, 2022 - degruyter.com
Abstract Machine learning and artificial intelligence are increasingly gaining in prominence
through image analysis, language processing, and automation, to name a few applications …
through image analysis, language processing, and automation, to name a few applications …
Python in chemistry: physicochemical tools
FV Ryzhkov, YE Ryzhkova, MN Elinson - Processes, 2023 - mdpi.com
The popularity of the Python programming language in chemistry is growing every year.
Python provides versatility, simplicity, and a rich ecosystem of libraries, making it the …
Python provides versatility, simplicity, and a rich ecosystem of libraries, making it the …
Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States
The prediction of reaction selectivity is a challenging task for computational chemistry, not
only because many molecules adopt multiple conformations but also due to the exponential …
only because many molecules adopt multiple conformations but also due to the exponential …
Comment on 'physics-based representations for machine learning properties of chemical reactions'
In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3
045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …
045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …
Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS
The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is
represented by the ensemble of its sampled conformations. Although some algorithms excel …
represented by the ensemble of its sampled conformations. Although some algorithms excel …
How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?
A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic
and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent …
and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent …
[HTML][HTML] Assessing the persistence of chalcogen bonds in solution with neural network potentials
Non-covalent bonding patterns are commonly harvested as a design principle in the field of
catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their …
catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their …
Rapid Rescoring and Refinement of Ligand–Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir
Virtual screening (VS) involves generation of poses for a library of ligands and ranking using
simplified energy functions and limited flexibility. Top-scored poses are used to rank and …
simplified energy functions and limited flexibility. Top-scored poses are used to rank and …
Machine learning assisted molecular modeling from biochemistry to petroleum engineering: A review
G Ma, J Shi, H **ong, C **ong, R Zhao… - Geoenergy Science and …, 2024 - Elsevier
Unconventional reservoirs have emerged as pivotal contributors, responsible for over 50%
of total US oil production. Yet, comprehending the intricate mechanisms of fluid transport in …
of total US oil production. Yet, comprehending the intricate mechanisms of fluid transport in …
From Organic Fragments to Photoswitchable Catalysts: The OFF–ON Structural Repository for Transferable Kernel-Based Potentials
Structurally and conformationally diverse databases are needed to train accurate neural
networks or kernel-based potentials capable of exploring the complex free energy …
networks or kernel-based potentials capable of exploring the complex free energy …