Transform Drug Discovery with Machine Learning and AI

BIOVIA Generative Therapeutics Design (GTD) is an agile cloud-based solution that improves lead optimization. GTD combines advanced data science, machine learning (ML), cheminformatics and structure-based modeling to explore chemical space and automate the virtual creation, testing and selection of novel compounds, identifying high-quality small molecule drug candidates faster.

Benefits of Generative Therapeutics Design

Leveraging AI and machine learning in lead generation and optimization offers numerous advantages.

Accelerated Compound Ideation

AI allows researchers to explore a large chemical space and investigate a diverse set of novel compounds rapidly. As a result, scientists can identify the most promising drug candidates for testing, reducing the time spent on running physical experiments.

Intellectual Property Consideration

AI and machine learning models can incorporate existing intellectual property into the drug design process, ensuring that new compounds do not infringe on existing patents. This consideration helps avoid legal challenges and streamlines the path to market.

Agile, Secure, and Cost-Effective Solution

Deploying AI in a secure, cloud-based environment offers an agile solution with a low total cost of ownership. This approach not only ensures data security but also provides scalability and flexibility, making GTD an attractive option for drug discovery.

Significant Cost Savings

By optimizing the drug discovery process and increasing the success rate of candidates, AI helps companies save millions in research dollars, while helping them gain a competitive edge by bringing novel therapies to market faster.

Shortened Lead Optimization Phase

By using AI-driven models, drug discovery teams can significantly shorten the lead optimization phase and identify the best candidates more quickly. This reduction in time accelerates the overall drug development timeline, bringing potential treatments to market faster.

Increased Success Rate in Preclinical and Clinical Studies

Discovery teams can tailor design lead compounds against complex target product profiles (TPPs) with AI and machine learning, increasing their likelihood of success in clinical trials. By predicting how drug candidates will perform in the patient and addressing potential concerns in the design phase, AI can reduce the risk of costly failures during later stages of development.

Accelerate Drug Discovery with AI

Combine Virtual Models with Real Data

BIOVIA Generative Therapeutics Design combines Virtual and Real (V+R) lead optimization to support the “active learning” innovation cycle:

Virtual cycles help explore chemical space by “learning” from real experiments. The system virtually screens and optimizes candidate compounds using a combination of Machine Learning (ML) models and structure-based modeling and simulation methods. Multi-parameter optimization algorithms balance competing objectives, including pharmacokinetics, pharmacodynamics, ADME (absorption, distribution, metabolism, excretion), bioavailability, drug metabolism, and more.
Real cycles uses the experimental data collected from the synthesis and testing of the most promising virtual compounds in the lab. This data is then used to improve predictive models and refine the exploration of chemical space.
These V+R active learning cycles continue until the identification of compounds with the desired target product profile (TPP).