Jun 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 6829-6836· 0 citations· 19 references
Computer ScienceMedicine
TL;DR
The Biotoxins Database (BioTD) is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species.
Abstract
Biotoxins, mainly produced by venomous animals, plants, and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources related to biotoxins are insufficient, particularly because of a lack of accurate and extensive activity data. To fulfill this demand, we developed the Biotoxins Database (BioTD). BioTD is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species. The activity data in BioTD are categorized into five groups: Activity, Safety, Kinetics, Hemolysis, and other physiological indicators. Moreover, BioTD provides data on 1,532 mutants, refines the whole sequence and signal peptide sequences of toxins, and annotates disulfide-bond information. All of the data in the database can be downloaded for free. Given the importance of biotoxins and their associated data, this new database is expected to attract broad interest from diverse research fields in drug discovery. BioTD is freely accessible at http://biotoxin.net/.
A revised release of the Unkown Mutation Analysis (XMAn) database is introduced by incorporating coding missense and nonsense mutations from the latest versions of the COSMIC Genome Screen Mutants and Cancer Gene Census datasets in two distinct FASTA-formatted peptide databases comprising 3,848,499 and 312,658 variants...
Joshua R. S. Haueis, I. Lazar· bioRxiv· 0 citations
This review systematically summarizes the latest research progress in druggable protein prediction algorithms, providing a comprehensive analysis across three dimensions: data resources, feature engineering, and predictive models.
Hong-Qi Zhang, Hong-Ling Wang, Shang-Hua Liu et al.· Current Drug Targets· 0 citations
The GPCR Evolution Database provides the data and tools for researchers to examine GPCR function through an evolutionary lens, allowing molecular insights from well-studied receptors to be extended to their underexplored relatives.
Atlantis, a database that integrates structural and functional information at the human proteome residue level and a Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research.
Natalia De Oliveira Rosa, Piergiorgio Ferronato, M. Varisco et al.· bioRxiv· 0 citations
The results showed that the QNL1 and QNL3 derivatives were the most favorable due to high intestinal absorption and apparent permeability and apparent permeability, showing potential as a future drug.
M. Sales, Caroline Do Nascimento Gonçalves, Abraão Lucas Silva dos Santos et al.· Discover Chemistry· 0 citations
Fermented products, such as cheeses, represent an increased interest mainly in their functional properties. The present database places particular emphasis on peptides, which are encrypted in proteins and subsequently released during proteolysis. Jihočeská Niva cheeses have been designated as Protected Geographical Ind...
S. T. Martín-del-Campo, A. Pérez-Alva, B. R. Shah et al.· Data in Brief· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 22, 2026
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026