Sigma profiles in deep learning: towards a universal molecular descriptor
DO Abranches, Y Zhang, EJ Maginn… - Chemical …, 2022 - pubs.rsc.org
This work showcases the remarkable ability of sigma profiles to function as molecular
descriptors in deep learning. The sigma profiles of 1432 compounds are used to train …
descriptors in deep learning. The sigma profiles of 1432 compounds are used to train …
PubChem3D: similar conformers
Background PubChem is a free and open public resource for the biological activities of small
molecules. With many tens of millions of both chemical structures and biological test results …
molecules. With many tens of millions of both chemical structures and biological test results …
FLASHFLOOD: a 3D field-based similarity search and alignment method for flexible molecules
A three-dimensional field-based similarity search and alignment method for flexible
molecules is introduced. The conformational space of a flexible molecule is represented in …
molecules is introduced. The conformational space of a flexible molecule is represented in …
[PDF][PDF] Chemiscope: interactive structure-property explorer for materials and molecules
The number of materials or molecules that can be created by combining different chemical
elements in various proportions and spatial arrangements is enormous. Computational …
elements in various proportions and spatial arrangements is enormous. Computational …
Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
Identifying bioactive conformations of small molecules is an essential process for virtual
screening applications relying on three-dimensional structure such as molecular docking …
screening applications relying on three-dimensional structure such as molecular docking …
DECIMER 1.0: deep learning for chemical image recognition using transformers
The amount of data available on chemical structures and their properties has increased
steadily over the past decades. In particular, articles published before the mid-1990 are …
steadily over the past decades. In particular, articles published before the mid-1990 are …
Reverse engineering chemical structures from molecular descriptors: how many solutions?
Physical, chemical and biological properties are the ultimate information of interest for
chemical compounds. Molecular descriptors that map structural information to activities and …
chemical compounds. Molecular descriptors that map structural information to activities and …
Chemixnet: Mixed dnn architectures for predicting chemical properties using multiple molecular representations
SMILES is a linear representation of chemical structures which encodes the connection
table, and the stereochemistry of a molecule as a line of text with a grammar structure …
table, and the stereochemistry of a molecule as a line of text with a grammar structure …
Learning molecular representations for medicinal chemistry: miniperspective
KV Chuang, LM Gunsalus… - Journal of Medicinal …, 2020 - ACS Publications
The accurate modeling and prediction of small molecule properties and bioactivities depend
on the critical choice of molecular representation. Decades of informatics-driven research …
on the critical choice of molecular representation. Decades of informatics-driven research …
Feature-map vectors: a new class of informative descriptors for computational drug discovery
GA Landrum, JE Penzotti, S Putta - Journal of computer-aided molecular …, 2006 - Springer
In order to develop robust machine-learning or statistical models for predicting biological
activity, descriptors that capture the essence of the protein–ligand interaction are required. In …
activity, descriptors that capture the essence of the protein–ligand interaction are required. In …