AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
Deep learning methods for molecular representation and property prediction
Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …
predict molecular property through diversified models.•One, two, and three-dimensional …
Application of computational biology and artificial intelligence in drug design
Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …
expense. Booming computational approaches, including computational biology, computer …
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
GW Kyro, RI Brent, VS Batista - Journal of Chemical Information …, 2023 - ACS Publications
Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
A point cloud-based deep learning strategy for protein–ligand binding affinity prediction
Y Wang, S Wu, Y Duan, Y Huang - Briefings in bioinformatics, 2022 - academic.oup.com
There is great interest to develop artificial intelligence-based protein–ligand binding affinity
models due to their immense applications in drug discovery. In this paper, PointNet and …
models due to their immense applications in drug discovery. In this paper, PointNet and …
Featurization strategies for protein–ligand interactions and their applications in scoring function development
The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …
Persistent Dirac for molecular representation
Molecular representations are of fundamental importance for the modeling and analysing
molecular systems. The successes in drug design and materials discovery have been …
molecular systems. The successes in drug design and materials discovery have been …
Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
With the great advancements in experimental data, computational power and learning
algorithms, artificial intelligence (AI) based drug design has begun to gain momentum …
algorithms, artificial intelligence (AI) based drug design has begun to gain momentum …
Prediction of binding free energy of protein–ligand complexes with a hybrid molecular mechanics/generalized born surface area and machine learning method
L Dong, X Qu, Y Zhao, B Wang - ACS omega, 2021 - ACS Publications
Accurate prediction of protein–ligand binding free energies is important in enzyme
engineering and drug discovery. The molecular mechanics/generalized Born surface area …
engineering and drug discovery. The molecular mechanics/generalized Born surface area …