neuralgap.io

Target Identification & Validation

Data Collection and Preprocessing

- Gather compound libraries and structural data
- Prepare molecular structures for virtual screening
- Standardize data formats for computational analysis

AI-Powered Virtual Screening

- Implement AI models for rapid compound screening
- Use machine learning algorithms to predict binding affinities
- Apply deep learning for structure-based virtual screening

Advanced Computational Methods

- Utilize GPU acceleration for molecular dynamics simulations
- Implement machine learning models for ADMET prediction
- Perform structure-based virtual screening using deep learning

Analysis and Hit Selection

- Evaluate virtual screening results
- Prioritize compounds based on predicted properties
- Select top hits for experimental validation

Powerful AI-Driven Hit Discovery

Advanced Compound Library Management

Integrate diverse chemical databases
Automated structure preparation and standardization
Enable flexible search and filtering capabilities
Support for custom libraries and proprietary compounds

AI-Powered Screening Engines

Explore Deep learning models for binding affinity prediction
Structure-based virtual screening using state-of-the-art algorithms
GPU-accelerated molecular dynamics simulations
Ensemble docking for improved accuracy

Comprehensive Hit Analysis Module

Machine learning-based ADMET property prediction
Interactive visualization of screening results
Automated hit clustering and similarity analysis
Integration with experimental validation data

Industry Leading GPU Cost Optimsation

>50% GPU Base Cost Optimization: We help you significantly reduce training expenses compared to traditional OEMs through advanced resource management, including;

Precision-optimized Deep Learning model training: Employ state-of-the-art quantization techniques to reduce model size and computational requirements without sacrificing accuracy.

Accelerated molecular dynamics: Utilize specialized algorithms, hardware optimizations, and parallel computing strategies to speed up simulations while preserving computational accuracy and physical fidelity.

Intelligent hyperparameter optimization: Utilize advanced Bayesian optimization and multi-fidelity algorithms to efficiently tune models, reducing the number of training runs and GPU hours required.

Adaptive learning rate schedules: Implement cutting-edge convergence acceleration techniques like cyclical learning rates and layer-wise adaptive rate scaling to achieve faster and more stable model convergence.

Experience accelerated results and substantial cost savings with our resource-efficient GPU compute processes.

Target Identification & Validation

Powerful AI-Driven Hit Discovery

Industry Leading GPU Cost Optimsation

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