AI-ACCELERATED DRUG DISCOVERY

Lysine-specific demethylase 4D

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Lysine-specific demethylase 4D - 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 Lysine-specific demethylase 4D 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 Lysine-specific demethylase 4D 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 Lysine-specific demethylase 4D, 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 Lysine-specific demethylase 4D. 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 Lysine-specific demethylase 4D. 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 Lysine-specific demethylase 4D 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.

Lysine-specific demethylase 4D

partner:

Reaxense

upacc:

Q6B0I6

UPID:

KDM4D_HUMAN

Alternative names:

JmjC domain-containing histone demethylation protein 3D; Jumonji domain-containing protein 2D; [histone H3]-trimethyl-L-lysine(9) demethylase 4D

Alternative UPACC:

Q6B0I6; B3KPC4; Q0VF39; Q9NT41; Q9NW76

Background:

Lysine-specific demethylase 4D, also known as JmjC domain-containing histone demethylation protein 3D, plays a pivotal role in the histone code by specifically demethylating 'Lys-9' of histone H3. This action is crucial as it does not affect other lysine residues such as H3 'Lys-4', H3 'Lys-27', H3 'Lys-36', nor H4 'Lys-20'. It targets both di- and trimethylated H3 'Lys-9' residues, without activity on monomethylated residues, generating formaldehyde and succinate in the process.

Therapeutic significance:

Understanding the role of Lysine-specific demethylase 4D could open doors to potential therapeutic strategies.

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