… issues and devise the most appropriate solution even problem was in a complex situation.
In this review, deep learning … Multi-task neural networks integrated into a platform called ‘…

… remaining challenges for the promising future of DL-based DTI prediction and de novo drug
design. … If this can be integrated, optimal drugs can be created from the early stages of new …

… , no review has yet summarized and integrated these methods … challenges facing future
developments of deep learning in … big data-driven pharmaceutical research and drug discovery, …

… , we focus on AI/ML solutions to key challenges such as BBB … the review by highlighting
challenges, limitations, and future … , the transfer learning models have been integrated into NN …

… [37] integrated drug chemical … machine learning division, deep learning has given a
significant boost and emerged as the leading technique for drug discovery and development in the …

… and high-throughput screening. However, AI techniques such as machine learning (ML)
and … The successful use of deep learning (DL) to predict the efficacy of drug compounds with …

… As machine learning is being adopted to tackle crucial issues in … [14-15] demonstrates
how machine learning can be integrated into G-protein coupled receptor (GPCR) ligand …

… discovering new biomarkers by analyzing RNA transcripts 47,108,109 . In fact, deep learning
… for cancer therapies 110 , so deep learning will have an impact on both drug discovery and …

… Many experimental drugs that were abandoned due to development issues or efficacy …
Generative deep learning networks can propose completely new molecules that exhibit the …

… real-world challenges of AI application in drug discovery, such … Reinforcement learning (RL)
is a robust machine learning … , particularly drug discovery and development (Vamathevan et …