Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the prol...
Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here...
Journal of chemical information and modeling
May 20, 2025
Molecular property prediction (MPP) plays a critical role in drug design and discovery. Due to the multimodal nature of molecular data (e.g., 1D SMILES strings and 2D molecular graphs), multimodal information fusion can generally achieve better perfo...
Topological indices are invariant quantitative metrics associated with a molecular graph, which characterize the bonding topology of a molecule. The main aim of analyzing topological indices is to summarize and transform chemical structural informati...
The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designi...
Journal of chemical information and modeling
May 1, 2025
Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges ...
Journal of chemical information and modeling
Apr 29, 2025
2,1,3-Benzothiadiazole (BTD) derivatives show promise in advanced photophysical applications, but designing molecules with optimal desired properties remains challenging due to complex structure-property relationships. Existing computational methods ...
The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged...
Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular ...
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...
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