AI-ACCELERATED DRUG DISCOVERY

Neurogenic differentiation factor 2

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Neurogenic differentiation factor 2 - 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 Neurogenic differentiation factor 2 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 Neurogenic differentiation factor 2 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 Neurogenic differentiation factor 2, 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 Neurogenic differentiation factor 2. 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 Neurogenic differentiation factor 2. 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 Neurogenic differentiation factor 2 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.

Neurogenic differentiation factor 2

partner:

Reaxense

upacc:

Q15784

UPID:

NDF2_HUMAN

Alternative names:

Class A basic helix-loop-helix protein 1; NeuroD-related factor

Alternative UPACC:

Q15784; Q8TBI7; Q9UQC6

Background:

Neurogenic differentiation factor 2, also known as NeuroD-related factor, plays a pivotal role in neuronal determination and differentiation. It acts as a transcriptional regulator, mediating calcium-dependent transcription activation and is crucial for the repression of neuronal differentiation genetic programs. This protein is involved in the development of various brain regions, including the cerebellum, hippocampus, and the hypothalamic-pituitary axis.

Therapeutic significance:

Given its involvement in Developmental and epileptic encephalopathy 72, a severe early-onset epilepsy with neurodevelopmental impairment, understanding the role of Neurogenic differentiation factor 2 could lead to novel therapeutic strategies targeting this and potentially other neurodevelopmental disorders.

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