Artificial intelligence and machine learning technology driven modern drug discovery and development
C Sarkar, B Das, VS Rawat, JB Wahlang… - International Journal of …, 2023 - mdpi.com
The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …
translational science effort that adds to human invulnerability and happiness. But advancing …
Artificial intelligence in drug discovery and development
KK Mak, YH Wong, MR Pichika - Drug discovery and evaluation: safety …, 2024 - Springer
This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …
discovery and development, encapsulating its potentials, methodologies, real-world …
[HTML][HTML] A review on machine learning approaches and trends in drug discovery
P Carracedo-Reboredo, J Liñares-Blanco… - Computational and …, 2021 - Elsevier
Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …
treatment of diseases. In the last years, the approach used in this search presents an …
DeepDTA: deep drug–target binding affinity prediction
Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …
drug discovery process. Most of the computational methods that have been proposed to …
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Motivation In bioinformatics, machine learning-based methods that predict the compound–
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
Applications of deep learning and reinforcement learning to biological data
Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …
new possibilities for life scientists to gather multimodal data in various application domains …
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …
and chemogenomics studies, and proteins without three-dimensional structure account for a …
Deep learning for health informatics
With a massive influx of multimodality data, the role of data analytics in health informatics
has grown rapidly in the last decade. This has also prompted increasing interests in the …
has grown rapidly in the last decade. This has also prompted increasing interests in the …
[HTML][HTML] Automating drug discovery
G Schneider - Nature reviews drug discovery, 2018 - nature.com
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in
which various characteristics of compounds—including efficacy, pharmacokinetics and …
which various characteristics of compounds—including efficacy, pharmacokinetics and …
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …