MOTIVATION: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent diversity among molecular properties, coupled with limited training data availability, poses challenges to the accuracy and generalizability of these methods beyond their training domain. RESULTS: In this work, we proposed a neural networks construction for high accurate and generalizable drug-target binding prediction, named Pre-trained Multi-view Molecular Representations (PMMR). The method uses pre-trained models to transfer representations of target proteins and drugs to the domain of drug-target binding prediction, mitigating the issue of poor generalizability stemming from limited data. Then, two typical representations of drug molecules, Graphs and SMILES strings, are learned respectively by a Graph Neural Network and a Transformer to achieve complementarity between local and global features. PMMR was evaluated on drug-target affinity and interaction benchmark datasets, and it derived preponderant performance contrast to peer methods, especially generalizability in cold-start scenarios. Furthermore, our state-of-the-art method was indicated to have the potential for drug discovery by a case study of cyclin-dependent kinase 2. AVAILABILITY AND IMPLEMENTATION: https://github.com/NENUBioCompute/PMMR.

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths globally, underscoring the critical need for early detection. Microbial biomarker detection offers significant advantages in revealing the physiological and pathological mechanisms of CRC, as well as in early screening. It provides not only deep insights into disease progression but also enables the possibility of non-invasive early diagnosis. The utilization of metagenomic shotgun sequencing (MGS) technology and the bioBakery workflow to analyze gut microbiome samples from six independent cohorts (n = 1291), including 645 CRC patients and 646 healthy controls (HC). Metagenomic analysis revealed a significant reduction in microbial diversity and identified 117 microbial species with altered abundance in CRC patients (p < 0.05). The DeepCRC model, incorporating key biomarkers and clinical metadata, achieved 97.70% accuracy and an AUC of 0.982 on an independent test set. SHAP analysis indicated that microbial species such as Ruminococcaceae unclassified SGB15260 and Clostridium leptum impact CRC significantly. These bacteria are associated with CRC through mechanisms involving inflammation and gut barrier dysfunction. The superior performance of the DeepCRC diagnostic model is expected to advance the development of non-invasive CRC screening methods. Source code is available at https://github.com/NENUBioCompute/DeepCRC.