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

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

In the early stages of drug discovery, predicting drug-target affinity is a crucial task. Due to the vast scale of genomic and chemical spaces, traditional biological methods are time-consuming, labor-intensive, and resource-demanding. As a result, machine learning-based computational methods have emerged to narrow down the pool of drug candidates. However, machine learning approaches still face several challenges in practical applications, particularly the scarcity of labeled samples and poor model generalization capability. To address these issues, this paper proposes a novel drug-target affinity prediction model, termed MetaBayes-DTA, based on an uncertainty-aware meta-learning framework. The model integrates the few-shot rapid adaptation capability of meta-learning with an uncertainty quantification mechanism to enhance prediction accuracy and reliability. MetaBayes-DTA is evaluated on two benchmark datasets, DAVIS and KIBA. Experimental results demonstrate that the proposed model outperforms existing methods.

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.

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

Synthetic data for more accurate deep learning models in molecular science: a test case of protein-ligand binding affinity prediction

This study shows that incorporating synthetic molecular dynamics data improves deep learning models for protein–ligand binding affinity prediction beyond static experimental structures, and highlights that dynamic synthetic datasets can enable deep learning models to outperform conventional methods such as MM-PBSA while remaining computationally efficient.

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

Machine learning applications in preclinical drug development have been focused on automated covariate selection in pharmacometric modeling and high-throughput screening processes early in drug discovery. While inherent drug property prediction has made significant improvements in the past decade, fusing early target-based drug discovery methods to preclinical stage pharmacokinetic (PK) property predictions has been limited. 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. We identified major obstacles hindering the development of superior prediction models for small molecule behavior in biological systems. These encompass data accessibility, quantity, and quality, architectural constraints such as poor interpretability and model inherent assumptions, and the lack of robust evaluation and uncertainty assessment methods. To mitigate data-related constraints, we advocate for the use of collaborative federated learning frameworks. Furthermore, we propose leveraging the pattern recognition capabilities of deep learning models in conjunction with the biological interpretability provided by mechanistic approaches to strike an optimal balance between accuracy and biological explainability guided by the intended application of the prediction model. Addressing these limitations will advance reliable modeling pipelines and enable effective extrapolation to novel chemical space, additional species, and emerging drug development scenarios.

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

ABSTRACT Mutation‐induced drug resistance challenges both pandemic surveillance and drug discovery. While experimental assays are resource‐intensive, current computational predictions remain limited by the scarcity of 3D mutant protein structures. We present DeepMutDTA, a structure‐independent model pre‐trained on 1.5 million data points to predict drug‐target affinity and uncover underlying interaction mechanisms. However, like other sequence‐based approaches, it often falls short in predicting mutant affinities due to the overwhelming sequence similarity between wild‐type (WT) and mutant (MT) targets. To bridge this gap, we introduce SimSiam‐MuTF, a novel fine‐tuning framework to enhance the detection of resistance variants by explicitly aligning latent embedding distances with the corresponding shifts in binding affinity between WT and MT targets. Compared to representative baselines, our model exhibits remarkable robustness across varied sequence identities and unseen data splits, yielding average performance gains of 2.47% (PCC) and 5.10% (SCC) in regression tasks, alongside 4.00% (AUC) and 4.17% (AUPR) in classification tasks. Applications to SARS‐CoV‐2, HIV‐1, and cancer‐related targets highlight its generalization potential and utility in informing therapeutic strategies against drug resistance. Collectively, this robust computational pipeline and fine‐tuning framework deepen our understanding of mutation‐induced resistance and may serve as a powerful platform to accelerate drug discovery against mutant targets.