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 Acid ceramidase 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 Acid ceramidase 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 Acid ceramidase, 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 Acid ceramidase. 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 Acid ceramidase. 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 Acid ceramidase 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.
Acid ceramidase
partner:
Reaxense
upacc:
Q13510
UPID:
ASAH1_HUMAN
Alternative names:
Acylsphingosine deacylase; N-acylethanolamine hydrolase ASAH1; N-acylsphingosine amidohydrolase; Putative 32 kDa heart protein
Alternative UPACC:
Q13510; E9PDS0; Q6W898; Q96AS2
Background:
Acid ceramidase, known by alternative names such as Acylsphingosine deacylase and N-acylsphingosine amidohydrolase, plays a pivotal role in the metabolism of sphingolipids. It hydrolyzes ceramides into sphingosine and free fatty acids, crucial for cell signaling pathways that regulate proliferation, apoptosis, and differentiation. This enzyme exhibits higher efficiency towards C12-ceramides and is involved in the synthesis of ceramides from fatty acids and sphingosine.
Therapeutic significance:
Acid ceramidase is linked to Farber lipogranulomatosis, a lysosomal storage disorder, and spinal muscular atrophy with progressive myoclonic epilepsy, highlighting its therapeutic potential. Targeting this protein could lead to novel treatments for these debilitating diseases.
