BIOVIA Generative Therapeutics Design
An Agile, Cloud-based Solution for AI-driven Drug Design
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).
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FAQ about AI in Drug Discovery
AI is used in drug discovery to analyze vast datasets, predict molecular interactions, and identify potential drug candidates faster than traditional methods. It enables more efficient screening of compounds and optimizes the drug design process.
AI improves success rates by accurately predicting how drug candidates will perform based on preclinical data. This reduces the likelihood of failures in later trial phases, saving time and resources.
AI tools can analyze existing patents and intellectual property to ensure that new drug designs do not infringe on existing rights. This integration helps streamline the design process while avoiding legal challenges.
Using AI in a cloud-based environment offers enhanced security through advanced encryption, regular updates, and real-time monitoring. It ensures that sensitive data is protected while enabling global collaboration and scalability.
Challenges include data quality and availability, the need for specialized expertise, and the integration of AI tools with existing workflows. Regulatory hurdles and the validation of AI-generated predictions also pose significant challenges.
BIOVIA Generative Therapeutics Design software is transforming the pharma industry by leveraging AI to streamline the drug discovery process. Through advanced data science and machine learning (ML) models, the platform accelerates lead optimization, helping pharmaceutical companies develop new therapies faster and more cost-effectively. By integrating AI into key phases of drug development, BIOVIA enables more efficient compound screening, structure-based modeling, and real-time data analysis.
BIOVIA Generative Therapeutics Design accelerates AI small molecule drug discovery by using machine learning (ML) and cheminformatics to explore chemical space more effectively. The platform automates the creation and testing of novel compounds, allowing researchers to rapidly identify high-quality small molecule drug candidates. This AI-driven approach optimizes lead discovery, significantly shortening the time it takes to identify promising compounds for further testing.
BIOVIA Generative Therapeutics Design dramatically reduces drug discovery timelines by automating critical tasks like compound screening, virtual testing, and lead optimization through AI. By accelerating the exploration of chemical space and improving the predictive accuracy of potential drug candidates, BIOVIA enables pharmaceutical industry and companies to bring new therapies to market faster.
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