Generative models for molecular discovery: Recent advances and challenges
Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …
While conventional molecular design involves using human expertise to propose …
Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
The paper presents a comprehensive overview of the use of artificial intelligence (AI)
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm
Deep learning is an important branch of artificial intelligence that has been successfully
applied into medicine and two-dimensional ligand design. The three-dimensional (3D) …
applied into medicine and two-dimensional ligand design. The three-dimensional (3D) …
Inverse design of porous materials using artificial neural networks
Generating optimal nanomaterials using artificial neural networks can potentially lead to a
notable revolution in future materials design. Although progress has been made in creating …
notable revolution in future materials design. Although progress has been made in creating …
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
RR Griffiths, JM Hernández-Lobato - Chemical science, 2020 - pubs.rsc.org
Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …
properties. The original scheme, featuring Bayesian optimization over the latent space of a …
Molecular design in drug discovery: a comprehensive review of deep generative models
Deep generative models have been an upsurge in the deep learning community since they
were proposed. These models are designed for generating new synthetic data including …
were proposed. These models are designed for generating new synthetic data including …
Molgensurvey: A systematic survey in machine learning models for molecule design
Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
A major challenge in materials design is how to efficiently search the vast chemical design
space to find the materials with desired properties. One effective strategy is to develop …
space to find the materials with desired properties. One effective strategy is to develop …
Targeted design of advanced electrocatalysts by machine learning
Exploring the production and application of clean energy has always been the core of
sustainable development. As a clean and sustainable technology, electrocatalysis has been …
sustainable development. As a clean and sustainable technology, electrocatalysis has been …
Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …
industry and research, where it has been utilized to efficiently identify new chemical entities …