409 research outputs found
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:
(i) how to explicitly model and learn local interactions between drugs and
targets for better prediction and interpretation; (ii) how to generalize
prediction performance on novel drug-target pairs from different distribution.
In this work, we propose DrugBAN, a deep bilinear attention network (BAN)
framework with domain adaptation to explicitly learn pair-wise local
interactions between drugs and targets, and adapt on out-of-distribution data.
DrugBAN works on drug molecular graphs and target protein sequences to perform
prediction, with conditional domain adversarial learning to align learned
interaction representations across different distributions for better
generalization on novel drug-target pairs. Experiments on three benchmark
datasets under both in-domain and cross-domain settings show that DrugBAN
achieves the best overall performance against five state-of-the-art baselines.
Moreover, visualizing the learned bilinear attention map provides interpretable
insights from prediction results.Comment: 16 pages, 6 figure
Structure-based drug discovery with deep learning
Artificial intelligence (AI) in the form of deep learning bears promise for
drug discovery and chemical biology, , to predict protein
structure and molecular bioactivity, plan organic synthesis, and design
molecules . While most of the deep learning efforts in drug
discovery have focused on ligand-based approaches, structure-based drug
discovery has the potential to tackle unsolved challenges, such as affinity
prediction for unexplored protein targets, binding-mechanism elucidation, and
the rationalization of related chemical kinetic properties. Advances in deep
learning methodologies and the availability of accurate predictions for protein
tertiary structure advocate for a in structure-based
approaches for drug discovery guided by AI. This review summarizes the most
prominent algorithmic concepts in structure-based deep learning for drug
discovery, and forecasts opportunities, applications, and challenges ahead
Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data
Development and homeostasis in multicellular systems both require exquisite
control over spatial molecular pattern formation and maintenance. Advances in
spatially-resolved and high-throughput molecular imaging methods such as
multiplexed immunofluorescence and spatial transcriptomics (ST) provide
exciting new opportunities to augment our fundamental understanding of these
processes in health and disease. The large and complex datasets resulting from
these techniques, particularly ST, have led to rapid development of innovative
machine learning (ML) tools primarily based on deep learning techniques. These
ML tools are now increasingly featured in integrated experimental and
computational workflows to disentangle signals from noise in complex biological
systems. However, it can be difficult to understand and balance the different
implicit assumptions and methodologies of a rapidly expanding toolbox of
analytical tools in ST. To address this, we summarize major ST analysis goals
that ML can help address and current analysis trends. We also describe four
major data science concepts and related heuristics that can help guide
practitioners in their choices of the right tools for the right biological
questions
- …