Accelerate Drug Development

With increased demand for novel therapies and personalized medicine in health care, Artificial Intelligence (AI) is revolutionizing pharmaceutical product development by accelerating drug discovery, streamlining lab operations, and personalizing biomanufacturing.

Improve target identification and biotherapeutic design for drug discovery. Enhance early pipeline growth. Harmonize methods for greater agility throughout the quality control and product release.

Technologies delivering Integrated Predictive Sciences, Small Molecule and Biologics Process Development, and Drug and Analytical Development facilitate data-driven decision making, enhance process control, and speed up the time to market.

Biologics Process Development

In the past year, approximately 25% of the FDA-approved drugs were biologics, with monoclonal antibodies accounting for over 50% of these approvals. While areas of research and development workflows are similar for biologics and small molecules, the complexity of biologics requires highly specialized processes and data analysis.

In silico techniques allowing for the design of biologics with optimized efficacy, toxicity, and drug properties helps save time and resources and improve the success rates for biopharma.


Small Molecule Process Development

The discovery cycle of small molecule drugs is tedious, time-consuming, and costly. As AI/ML-based generative methods are maturing to improve this cycle, their utility and impact is likely to permeate many aspects of drug discovery, including lead identification and optimization.

Integrating AI/ML into drug discovery can significantly improve R&D productivity and help scientists develop efficacious treatments faster, at lower costs, and more efficiently than ever before.

Analytical Development

Procedures represent any number of processes, such as a synthetic chemistry or formulation recipe, an analytical method, a standard operating procedure, or a protocol.

Ensuring consistency in procedures reduces re-work, streamlines workflows, standardizes data, and removes barriers in tech transfer to manufacturing.

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Adopting an end-to-end solution for product development, manufacturing, and supply chain management will accelerate drug development.

Optimize resource utilization, reduce costs, improve collaboration and efficiency, and speed time-to-market.

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