Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …
significantly reduces the time and cost required for developing novel drugs. With the …
Recent advances in deep learning for retrosynthesis
Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
Chemformer: a pre-trained transformer for computational chemistry
Transformer models coupled with a simplified molecular line entry system (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics …
recently proven to be a powerful combination for solving challenges in cheminformatics …
Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework
The clinical efficacy and safety of a drug is determined by its molecular properties and
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …
Large-scale chemical language representations capture molecular structure and properties
J Ross, B Belgodere, V Chenthamarakshan… - Nature Machine …, 2022 - nature.com
Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …
property predictions, which is of interest in drug discovery and material design. Various …
Hierarchical molecular graph self-supervised learning for property prediction
X Zang, X Zhao, B Tang - Communications Chemistry, 2023 - nature.com
Molecular graph representation learning has shown considerable strength in molecular
analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre …
analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre …
SELFormer: molecular representation learning via SELFIES language models
Automated computational analysis of the vast chemical space is critical for numerous fields
of research such as drug discovery and material science. Representation learning …
of research such as drug discovery and material science. Representation learning …
XGraphBoost: extracting graph neural network-based features for a better prediction of molecular properties
D Deng, X Chen, R Zhang, Z Lei… - Journal of chemical …, 2021 - ACS Publications
Determining the properties of chemical molecules is essential for screening candidates
similar to a specific drug. These candidate molecules are further evaluated for their target …
similar to a specific drug. These candidate molecules are further evaluated for their target …
A fingerprints based molecular property prediction method using the BERT model
N Wen, G Liu, J Zhang, R Zhang, Y Fu… - Journal of Cheminformatics, 2022 - Springer
Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep
learning-based MPP models capture molecular property-related features from various …
learning-based MPP models capture molecular property-related features from various …
From intuition to AI: evolution of small molecule representations in drug discovery
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify
molecular starting points that will develop into safe and efficacious drugs while reducing …
molecular starting points that will develop into safe and efficacious drugs while reducing …