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 Platelet-activating factor acetylhydrolase 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 Platelet-activating factor acetylhydrolase 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 Platelet-activating factor acetylhydrolase, 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 Platelet-activating factor acetylhydrolase. 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 Platelet-activating factor acetylhydrolase. 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 Platelet-activating factor acetylhydrolase 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.
Platelet-activating factor acetylhydrolase
partner:
Reaxense
upacc:
Q13093
UPID:
PAFA_HUMAN
Alternative names:
1-alkyl-2-acetylglycerophosphocholine esterase; 2-acetyl-1-alkylglycerophosphocholine esterase; Group-VIIA phospholipase A2; LDL-associated phospholipase A2; PAF 2-acylhydrolase
Alternative UPACC:
Q13093; A5HTT5; Q15692; Q5VTT1; Q8IVA2
Background:
Platelet-activating factor acetylhydrolase, known by its alternative names such as 1-alkyl-2-acetylglycerophosphocholine esterase and Group-VIIA phospholipase A2, plays a pivotal role in phospholipid catabolism during inflammatory and oxidative stress response. It specifically targets phospholipids, hydrolyzing the ester bond of fatty acyl groups, and is instrumental in inactivating platelet-activating factor (PAF), a potent pro-inflammatory signaling lipid.
Therapeutic significance:
The enzyme's deficiency is linked to exacerbated responses to inflammatory agents, manifesting in diseases like Platelet-activating factor acetylhydrolase deficiency, Asthma, and Atopic hypersensitivity. Understanding its role could lead to novel therapeutic strategies for these conditions, highlighting its importance in drug discovery for inflammatory and respiratory diseases.
