AI-ACCELERATED DRUG DISCOVERY

Ribonuclease SLFN12

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Ribonuclease SLFN12 - Focused Library Design

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 Ribonuclease SLFN12 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 Ribonuclease SLFN12 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 Ribonuclease SLFN12, 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 Ribonuclease SLFN12. 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 Ribonuclease SLFN12. 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 Ribonuclease SLFN12 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.

Ribonuclease SLFN12

partner:

Reaxense

upacc:

Q8IYM2

UPID:

SLN12_HUMAN

Alternative names:

Schlafen family member 12

Alternative UPACC:

Q8IYM2; A8K711; Q9NP47

Background:

Ribonuclease SLFN12, also known as Schlafen family member 12, plays a pivotal role in an E2/17beta-estradiol-induced pro-apoptotic signaling pathway. This pathway, activated by the stabilization of the PDE3A/SLFN12 complex and subsequent dephosphorylation of SLFN12, is crucial for its ribosomal RNA/rRNA ribonuclease activity, particularly in tissues with high E2 concentrations.

Therapeutic significance:

Understanding the role of Ribonuclease SLFN12 could open doors to potential therapeutic strategies, especially considering its involvement in apoptosis and cell differentiation.

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