Machine-learning methods for ligand–protein molecular docking
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 …
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
Interpretable bilinear attention network with domain adaptation improves drug–target prediction
Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …
methods show promising performance, but two challenges remain: how to explicitly model …
CFSSynergy: combining feature-based and similarity-based methods for drug synergy prediction
Drug synergy prediction plays a vital role in cancer treatment. Because experimental
approaches are labor-intensive and expensive, computational-based approaches get more …
approaches are labor-intensive and expensive, computational-based approaches get more …
[HTML][HTML] 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 develo** novel drugs. With the …
significantly reduces the time and cost required for develo** novel drugs. With the …
DeepTraSynergy: drug combinations using multimodal deep learning with transformers
Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
How to produce expressive molecular representations is a fundamental challenge in
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction
Informative representation of molecules is a crucial prerequisite in AI-driven drug design and
discovery. Pharmacophore information including functional groups and chemical reactions …
discovery. Pharmacophore information including functional groups and chemical reactions …
ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions
Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to
novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta …
novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta …
Large language models in bioinformatics: applications and perspectives
Large language models (LLMs) are a class of artificial intelligence models based on deep
learning, which have great performance in various tasks, especially in natural language …
learning, which have great performance in various tasks, especially in natural language …