Sep 2026· Methods in molecular biology· Vol 3074, pp.
39-51
· 0 citations
Medicine
TL;DR
Two deep learning-based computational protocols designed to address drug mechanisms of action and predicting drug-target interactions are presented, offering scalable, cost-effective tools for accelerating therapeutic development by bridging chemical structure, molecular interactions, and systems-level biological responses.
This review systematically analyzes key challenges in DDI prediction: balancing model performance with interpretability, integrating heterogeneous features, ensuring sample set reliability, and optimizing algorithmic architectures, and outlines promising directions for future innovation.
Yun Zuo, Jia-Yi Ji, Yuwen Li et al.· Current Bioinformatics· 0 citations
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large-scale, rapid DDI prediction; however, current approaches suffer from...
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
Kamil Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 1 citation
Combination therapy is a common strategy in cancer treatment to improve efficacy, minimize side effects, and overcome drug resistance. However, the vast number of possible drug pairings makes experimental screening both time-consuming and costly. Drug interactions can result in synergy and antagonism, emphasizing the n...
Azar Shamloo, Jack Tuszynski, Yun Tam et al.· International Journal of Mol...· 0 citations
Results indicate simple architectural modifications to traditional convolution methods may be sufficient to bridge the gap to large pre-trained PLMs, and an architectural modification of the baseline convolution method designed to improve protein representation learning is evaluated.
Matija Marijan, Ivan Tanasijević· bioRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
