The Pharma.AI platform combining deep generative models (GAN), reinforcement learning (RL), transformers, and other modern machine learning techniques enables the identification of novel targets, generation of novel molecules non-existent in the chemical space, and prediction of outcomes for clinical trials.
SAN ANTONIO, Jan. 25, 2022 /PRNewswire/ -- Based on its recent analysis of the global artificial intelligence (AI)-enabled drug discovery industry, Frost & Sullivan recognizes Insilico Medicine with the 2022 Entrepreneurial Company of the Year Award in the global AI-enabled drug discovery industry.
Pharma.AI, the end-to-end AI-enabled drug discovery and development platform of Insilico, addresses challenges that touch on chemistry, biology, and digital medicine. The combined AI and drug discovery expertise enable the identification of novel targets, generation of novel molecules non-existent in the chemical space, and prediction of outcomes for clinical trials. The AI solutions and verified automated AI platform streamline research and development (R&D) efforts, remodel therapeutics discovery, and advance precision medicine. In addition, these solutions accelerate research workflow, leading to rapid and cost-effective discovery and development.
According to Supriya Lala Kundu, industry analyst at Frost & Sullivan, "Insilico's AI platforms integrate GANs, reinforcement learning (RL), and transformer neural networks to automate multi-targeting, polypharmacolocogy, and holistic diseases assessment. The company is working on AI-powered robotics to create driverless drug discovery as it moves towards advanced technologies and 100% automation."
PandaOmics is one of the AI-enabled engines of Pharma.AI which integrates proteomics, transcriptomics, and other data types with advanced AI algorithms. PandaOmics supports target disease identification and supports research and target identification for different diseases. The AI-powered platform provides complex algorithms that recommend relevant novel drug target hypotheses in a few days rather than several months, significantly reducing drug discovery timelines.
Insilico Medicine also utilizes multiple algorithms that rapidly evaluate the targets for possible binding sites to address the challenge of transitioning from target to small molecules with desired properties. Chemistry42, a small molecule generation engine of Pharma.AI platform with 30 algorithms, identifies the right pockets and designs a range of molecules that do not exist in the chemical space within a few days. In addition, the engine enables the filtering from billions of generated molecules to identify easy-to-synthesize and inexpensive-to-develop molecules with the highest success potential. Chemistry42TM , a cloud and on-premise hardware-agnostic and scalable platform that can be licensed and tested by clients, delivers new actionable drug-like molecules in days.
"Insilico leverages its proprietary AI-driven platforms to effectively address the challenges of identifying accurate targets, developing novel molecular structures with desired parameters, and predicting clinical trial outcomes. Its deep technical expertise facilitates accelerated and cost-efficient novel therapeutic compound design and innovative drug discovery for cancer, fibrosis, infectious diseases, and aging-related diseases. For example, in 2021, Insilico initiated the first-in-human study of a potentially first-in-class drug candidate with a novel target for fibrosis. Notably, the company completed the entire discovery process from target discovery to preclinical candidate nomination within 18 months on a budget of $2.6 million. Furthermore, Insilico nominated two preclinical candidates with a novel molecular structure for anemia of chronic kidney disease and inflammatory bowel disease within 12 months using its AI engine. Such milestones thoroughly validate Insilico's AI platform as robust and effective," Kundu explained further.
In addition to the two engine mentioned above that have already been launched as software, InClinicoTM , another AI-powered engine in development by Insilico which could provide prediction of outcomes for clinical trials has exciting prospects as well. With its strong overall performance, growing partnerships, focus on innovation, and expertise in end-to-end drug discovery and development, Insilico Medicine earns Frost & Sullivan's 2022 Entrepreneurial Company of the Year Award in the global AI-enabled drug discovery industry.
Each year, Frost & Sullivan presents this award to the company that has demonstrated excellence in devising and implementing a strong growth strategy. The recipient has shown strength in terms of innovation in products and technologies, leadership in customer value, and speed in response to market needs. The award looks at the emerging market participants in the industry and recognizes their best practices that are positioned for future growth excellence.
Frost & Sullivan Best Practices Awards recognize companies in various regional and global markets for demonstrating outstanding achievement and superior performance in leadership, technological innovation, customer service, and strategic product development. Industry analysts compare market participants and measure performance through in-depth interviews, analyses, and extensive secondary research to identify best practices in the industry.
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About Insilico Medicine
Insilico Medicine, an end-to-end AI-driven drug discovery and development company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. Insilico Medicine has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS) diseases and aging-related diseases. For more information, visit www.insilico.com.
SOURCE Frost & Sullivan