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[HTML][HTML] Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems
Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful
computational method for fundamental research in science branches such as biology …
computational method for fundamental research in science branches such as biology …
[HTML][HTML] CADD, AI and ML in drug discovery: A comprehensive review
D Vemula, P Jayasurya, V Sushmitha, YN Kumar… - European Journal of …, 2023 - Elsevier
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest
because of its potential to expedite and lower the cost of the drug development process …
because of its potential to expedite and lower the cost of the drug development process …
Machine-learning methods for ligand–protein molecular docking
K Crampon, A Giorkallos, M Deldossi, S Baud… - Drug discovery today, 2022 - Elsevier
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains
use AI, including molecular simulation for drug discovery. In this review, we provide an …
use AI, including molecular simulation for drug discovery. In this review, we provide an …
Protein assembly by design
Proteins are nature's primary building blocks for the construction of sophisticated molecular
machines and dynamic materials, ranging from protein complexes such as photosystem II …
machines and dynamic materials, ranging from protein complexes such as photosystem II …
Concepts of artificial intelligence for computer-assisted drug discovery
X Yang, Y Wang, R Byrne, G Schneider… - Chemical …, 2019 - ACS Publications
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …
opportunities for the discovery and development of innovative drugs. Various machine …
PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …
computational chemistry. After being trained on appropriate ab initio reference data, these …
Carbon nanodots from an in silico perspective
Carbon nanodots (CNDs) are the latest and most shining rising stars among
photoluminescent (PL) nanomaterials. These carbon-based surface-passivated …
photoluminescent (PL) nanomaterials. These carbon-based surface-passivated …
Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors
The SARS-CoV-2 is the causative agent of COVID-19 pandemic that is causing a global
health emergency. The lack of targeted therapeutics and limited treatment options have …
health emergency. The lack of targeted therapeutics and limited treatment options have …
Modeling and simulations of polymers: a roadmap
Molecular modeling and simulations are invaluable tools for the polymer science and
engineering community. These computational approaches enable predictions and provide …
engineering community. These computational approaches enable predictions and provide …
The Rosetta all-atom energy function for macromolecular modeling and design
Over the past decade, the Rosetta biomolecular modeling suite has informed diverse
biological questions and engineering challenges ranging from interpretation of low …
biological questions and engineering challenges ranging from interpretation of low …