AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Overall, SPB-Seeker provides an extensible computational framework for targeted short peptide binder discovery and offers a basis for subsequent affinity optimization and stability enhancement.
This study aims to design and evaluate novel therapeutic binders targeting the ATP-binding region of the AbcA efflux transporter protein in Aspergillus fumigatus. By inhibiting ATP binding, these binders seek to prevent AbcA-mediated azole resistance and restore the effectiveness of antifungal treatments. The research...
Tuberculosis remains one of the world's most significant infectious diseases, with approximately 10 million new cases annually. The polyketide synthase 13 (Pks13) enzyme, responsible for the final cyclopropanation step of mycolic acid biosynthesis, has remained understudied as an antituberculous drug target despite its...
An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.
A scalable, machine-learning-integrated virtual screening framework designed to explore ultra-large chemical space spanning an input search space of approximately 884 million compounds from ZINC20 and 199,854 purchasable compounds from the SPECS database is reported.
An integrated computational workflow to identify natural small molecules with high potential to bind protein kinase B (PknB) identifies promising PknB inhibitors for further experimental validation and offers an efficient platform to accelerate natural‐product‐based anti‐TB drug discovery.
Cardiovascular thromboses bring heavy global health burdens, while traditional screening of food-derived antithrombotic peptides is inefficient. This work built an integrated pipeline integrating de novo peptide generation, hybrid LSTM-GCN deep learning, and molecular simulations, screening 15,000 sequences to obtain l...
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