AI-ACCELERATED DRUG DISCOVERY

Serine/threonine-protein kinase mTOR

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Serine/threonine-protein kinase mTOR - 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 Serine/threonine-protein kinase mTOR 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 Serine/threonine-protein kinase mTOR 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 Serine/threonine-protein kinase mTOR, 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 Serine/threonine-protein kinase mTOR. 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 Serine/threonine-protein kinase mTOR. 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 Serine/threonine-protein kinase mTOR 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.

Serine/threonine-protein kinase mTOR

partner:

Reaxense

upacc:

P42345

UPID:

MTOR_HUMAN

Alternative names:

FK506-binding protein 12-rapamycin complex-associated protein 1; FKBP12-rapamycin complex-associated protein; Mammalian target of rapamycin; Mechanistic target of rapamycin; Rapamycin and FKBP12 target 1; Rapamycin target protein 1

Alternative UPACC:

P42345; Q4LE76; Q5TER1; Q6LE87; Q96QG3; Q9Y4I3

Background:

Serine/threonine-protein kinase mTOR, also known as the mechanistic target of rapamycin, plays a pivotal role in cellular metabolism, growth, and survival. This protein is a central regulator, responding to various signals such as hormones, growth factors, and stress. mTOR functions through two distinct complexes, mTORC1 and mTORC2, influencing over 800 proteins through direct or indirect phosphorylation.

Therapeutic significance:

mTOR's involvement in diseases like Smith-Kingsmore syndrome and Focal cortical dysplasia 2 highlights its therapeutic potential. Targeting mTOR pathways could offer new strategies for treating these conditions, emphasizing the importance of understanding mTOR's role in disease mechanisms.

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