Recent neuroscience and computational biology research has advanced understanding in several areas related to neurological disorders and drug discovery. Studies have demonstrated that the mGluR5 agonist CHPG enhances differentiation of human oligodendrocytes, while transplantation of GABAergic interneuron progenitors has restored cortical circuit function in an Alzheimer's disease mouse model. Peptides have been shown to alleviate cognitive impairment by inhibiting and disassembling amyloid-beta aggregates, which are implicated in Alzheimer's disease pathology. Additional research revealed that atypical antipsychotics alter microglial functions through astrocyte-derived extracellular vesicles. Dietary interventions have ameliorated abnormal synaptic proteomes and defective behaviors in autism mouse models. Machine learning approaches have been applied to drug discovery, including QSAR modeling of anti-cancer activity from synthetic flavones, in silico screening of small molecule inhibitors targeting amyloid-beta aggregation, and prediction of kinase inhibitors by integrating compound and protein features. New deep learning frameworks such as DeepDTAGen enable multitask prediction of drug-target affinities and generation of target-aware drugs. Protein language models have been extended to viral genomic scales using biologically induced sparse attention, allowing processing of entire viral genomes on single GPUs. Other studies have explored neural coding of speech, perceptual decision-making, and structural brain connectivity in psychosis. Overall, these interdisciplinary efforts combine biological experiments and computational methods to enhance understanding of brain function, neurodegenerative diseases, and drug development.