AI-ACCELERATED DRUG DISCOVERY
Available from Reaxense
This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of Serum paraoxonase/lactonase 3 including:
1. LLM-powered literature research
Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into Serum paraoxonase/lactonase 3 therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.
Fig. 1. Preliminary target research workflow
2. AI-Driven Conformational Ensemble Generation
Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of Serum paraoxonase/lactonase 3, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.
Fig. 2. AI-powered molecular dynamics simulations workflow
3. Binding pockets identification and characterization
We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.
Fig. 3. AI-based binding pocket detection workflow
4. AI-Powered Virtual Screening
Our ecosystem is equipped to perform AI-driven virtual screening on Serum paraoxonase/lactonase 3. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of Serum paraoxonase/lactonase 3. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.
Fig. 4. The screening workflow of Receptor.AI
Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.
The focused library for Serum paraoxonase/lactonase 3 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.
Serum paraoxonase/lactonase 3
partner:
Reaxense
upacc:
Q15166
UPID:
PON3_HUMAN
Alternative names:
-
Alternative UPACC:
Q15166; A4D1H8; O75855; O76060; Q6IRU9; Q8IX97; Q9BZH9
Background:
Serum paraoxonase/lactonase 3, encoded by the gene with accession number Q15166, exhibits enzymatic activity towards a variety of substrates, including organophosphates and aromatic carboxylic acid esters. It is particularly effective in hydrolyzing lactones, such as those found in statin prodrugs like lovastatin, as well as aromatic lactones and certain ring lactones with aliphatic substituents.
Therapeutic significance:
Understanding the role of Serum paraoxonase/lactonase 3 could open doors to potential therapeutic strategies. Its ability to hydrolyze various lactones, including statin prodrugs, suggests a potential role in modulating drug efficacy and metabolism, which could be pivotal in the development of treatments for cardiovascular diseases and beyond.
