Pelitinib

Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets─Davis, KIBA, and Metz─and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.

Chemotherapy-induced peripheral neuropathy (CIPN) is a disabling side effect of cancer chemotherapy that can often limit treatment options for cancer patients or have life-long neurodegenerative consequences that reduce the patient’s quality of life. CIPN is caused by the detrimental actions of various chemotherapeutic agents on peripheral axons. Currently, there are no approved preventative measures or treatment options for CIPN, highlighting the need for the discovery of novel therapeutics and improving our understanding of disease mechanisms. In this study, we utilized human-induced pluripotent stem cell (hiPSC)-derived motor neurons as a platform to mimic axonal damage after treatment with vincristine, a chemotherapeutic used for the treatment of breast cancers, osteosarcomas, and leukemia. We screened a total of 1902 small molecules for neuroprotective properties in rescuing vincristine-induced axon growth deficits. From our primary screen, we identified 38 hit compounds that were subjected to secondary dose response screens. Six compounds showed favorable pharmacological profiles – AZD7762, A-674563, Blebbistatin, Glesatinib, KW-2449, and Pelitinib, all novel neuroprotectants against vincristine toxicity to neurons. In addition, four of these six compounds also showed efficacy against vincristine-induced growth arrest in human iPSC-derived sensory neurons. In this study, we utilized high-throughput screening of a large library of compounds in a therapeutically relevant assay. We identified several novel compounds that are efficacious in protecting different neuronal subtypes from the toxicity induced by a common chemotherapeutic agent, vincristine which could have therapeutic potential in the clinic.