[HTML][HTML] Deep learning for deep chemistry: optimizing the prediction of chemical patterns
Computational Chemistry is currently a synergistic assembly between ab initio calculations,
simulation, machine learning (ML) and optimization strategies for describing, solving and …
simulation, machine learning (ML) and optimization strategies for describing, solving and …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Deep learning in chemistry
Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …
learning is a type of machine learning that uses a hierarchical recombination of features to …
When machine learning meets molecular synthesis
The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …
Machine learning the ropes: principles, applications and directions in synthetic chemistry
Machine learning (ML) has emerged as a general, problem-solving paradigm with many
applications in computer vision, natural language processing, digital safety, or medicine. By …
applications in computer vision, natural language processing, digital safety, or medicine. By …
Geometric deep learning for structure-based ligand design
A pervasive challenge in drug design is determining how to expand a ligand─ a small
molecule that binds to a target biomolecule─ in order to improve various properties of the …
molecule that binds to a target biomolecule─ in order to improve various properties of the …
Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions
The optimal design of compounds through manipulating properties at the molecular level is
often the key to considerable scientific advances and improved process systems …
often the key to considerable scientific advances and improved process systems …
Molecular machine learning for chemical catalysis: Prospects and challenges
Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …
MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
PO Dral, F Ge, YF Hou, P Zheng, Y Chen… - Journal of Chemical …, 2024 - ACS Publications
Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …
At the same time, the rapid development of ML methods requires a flexible software …
Pairwise difference regression: a machine learning meta-algorithm for improved prediction and uncertainty quantification in chemical search
Machine learning (ML) plays a growing role in the design and discovery of chemicals,
aiming to reduce the need to perform expensive experiments and simulations. ML for such …
aiming to reduce the need to perform expensive experiments and simulations. ML for such …