Neuralgap offers an AI-powered platform that accelerates drug discovery by integrating diverse molecular data. Its Genesys multi-modal AI engine predicts binding affinity and bioactivity, streamlines hit discovery, and optimizes lead candidates through intelligent scaling and adaptive learning.
Funding
Funding not disclosed
Founders
Product
Problem
Drug discovery and development processes are often lengthy and computationally intensive, leading to significant delays in identifying viable therapeutic candidates. Traditional methods struggle to efficiently integrate diverse molecular data types and predict complex biological interactions, hindering the optimization of drug properties.
Solution
Neuralgap provides an AI-powered platform designed to accelerate drug discovery and development. Their core offering, the Genesys multi-modal AI engine, integrates various molecular representations and dynamic data for unified processing. This engine facilitates advanced pattern recognition across binding modalities and employs adaptive learning to continuously improve predictions from real-world screening results. Genesys enables intelligent scaling by reducing computational parameters and enhancing accuracy with fewer training compounds, streamlining hit discovery, optimization, and lead candidate selection. The platform also supports custom deep learning builds for pharmaceutical clients and offers AI-driven genomics analysis and literature mining for target identification.
Target Audience
Primary customers include pharmaceutical companies, biotechnology firms, and contract research organizations (CROs) engaged in drug discovery and development. The platform also supports biotech investment analysts with industry insights.
Features
- Genesys multi-modal AI engine for predicting binding affinity and bioactivity.
- Unified processing of multiple molecular representations and real-time synchronization of structural and dynamic data.
- Adaptive learning capabilities trained on asynchronous molecular data with continuous improvement from screening results.
- Intelligent scaling features that reduce computational parameters and improve accuracy with fewer training compounds.
- AI-powered virtual screening, including structure-based virtual screening using deep learning.
- Machine learning for ADMET prediction and molecular dynamics simulations with GPU acceleration.
- Quantitative structure-activity relationship (QSAR) modeling using AI.
- AI-driven toxicity prediction and safety assessment.
- AI-driven genomics data analysis and natural language processing for literature mining for target identification.
- Support for custom deep learning builds for pharmaceutical clients.