A Drug-Target Affinity Prediction Model Based on Bayesian Meta-Learning and Uncertainty Fusion

Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction

It is indicated that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings, and most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization.

Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.

This scoping review investigates the current state of PK property prediction of small molecules in drug discovery using machine learning methods and a combination of machine learning and mechanistic models and proposes leveraging the pattern recognition capabilities of deep learning models in conjunction with the biological interpretability provided by mechanistic approaches.

Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.

Quantitative prediction of inhibitor potency can accelerate early-stage drug discovery. Recently, data-driven approaches have gained widespread interest in drug discovery, as evidenced by a growing number of benchmarking challenges and open competitions. In this context, we developed a machine learning-based methodology that can find the most effective way of predicting IC50 values against ASK1 from SMILES, for "Jump AI(.py) 2025: 3rd AI Drug Discovery Competition", hosted by the Korea Pharmaceutical and Bio-Pharma Manufacturers Association (KPBMA) on the Dacon platform. Applying our methodology achieved the highest overall predictive performance among all participating teams. Beyond this competition setting, we present a compact SMILES-based modeling workflow comprising (i) a pre-trained encoder, (ii) regression models, (iii) data augmentation, and (iv) hyperparameter tuning. We systematically compared molecular representations from sequence- and graph-based models, including ChemBERTa-2 and MolCLR. Across encoder-regressor combinations, ChemBERTa-77 M-MLM embeddings paired with support vector regression (SVR) yielded the strongest predictive performance. Embedding-level mix-up augmentation and SVR hyperparameter tuning further improved predictive performance. Our findings highlight that careful SMILES preprocessing and encoder selection have a critical influence on IC50 values and provide a reproducible benchmark for single-target bioactivity prediction, thus contributing to a more efficient drug discovery process. Scientific Contribution In this study, we propose a machine learning methodology for predicting the IC50 values of ASK1 inhibitors from SMILES representations, with a systematic comparison of molecular encoders and regression models. Our results show that the use of suitable encoder-regressor pairs together with embedding-level mix-up augmentation improves model generalizability without requiring SMILES-level augmentation. This strategy would be particularly useful for settings with imbalanced labels or limited data, and could be applied more broadly to IC50 prediction for other kinase inhibitors.

An Interpretable Deep Learning Framework with Dual-Modality Feature Fusion for Accurate Drug-Target Affinity Prediction

Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This research presents Dual modality feature fused-drug target affinity (DMFF-DTA), a model for drug-target affinity anticipation using dual-modality neural networks that considers both the sequence and graph structure of medicines and proteins. To facilitate more exact and efficient drug-target interaction modeling, the model incorporates a binding site-focused graph generation method for extracting binding information. Experimental results show that DMFF-DTA is far more effective than current state-of-the-art approaches. By outperforming state-of-the-art approaches by more than 8%, the model demonstrates remarkable generalizability to hitherto unexplored medicines and targets. The model's biological relevance is confirmed by the model interpretability analysis. This paper presents a reliable and understandable method for improving computational drug discovery by integrating multi-view protein and drug properties.

Prediction of Drug Target Interactions Based on Artificial Intelligence

Drug-target interaction (DTI) prediction is an efficient pre-screening method that uses algorithmic models to assess the binding potential of drug molecules to protein targets. Current research is accelerating towards the integration of heterogeneous graph neural networks, protein language models, and generative artificial intelligence. This review systematically summarizes the latest developments in these technologies, pointing out the problems currently being addressed in research such as data sparsity and cold start, as well as the manifestations of general machine learning challenges such as recommendation systems and noise learning in the biomedical field; Interpret representative models such as Dual Heterogeneous Graph Transformer for Drug-Target Interaction (DHGT-DTI) and Graph Positional encoding and Sequence features for Drug-Target Interaction (GPS-DTI). The combination of dual perspective learning, equivariant graph convolution, and attention mechanism enhances the understanding and reasoning ability of these models in complex biological networks. The generative artificial intelligence diffusion model has opened up a path for developing new drugs through structured and data enhanced approaches. Research has shown that important issues related to computational performance, interpretability, and data compatibility still need to be addressed in existing models. Building a high-performance, multifunctional pre trained model for large-scale biomolecules should be a key direction for development.

Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.