Artificial intelligence (AI) is rapidly revolutionizing the field of drug discovery, offering new possibilities to improve the efficacy and safety of pharmaceutical development. With an estimated 90% failure rate in clinical trials, the integration of AI and machine learning in the drug discovery process holds significant promise for the industry.
AI, a computer’s ability to mimic human intelligence, and machine learning, a subcategory of AI that uses preexisting data to find solutions to complex problems, enable computers to identify patterns, make decisions, and learn without direct instruction. This transformative technology allows for the development of AI-based drugs with enhanced efficacy features such as target specificity and optimized binding.
One pioneering company harnessing the power of AI is Salve Therapeutics, which is using the technology to explore the human virome. Stefan N. Lukianov, CEO of Salve Therapeutics, explains, “Every tissue in the human body has a range of co-evolved viruses,” and these naturally occurring viruses can be used as a carrier for gene therapy to treat rare genetic diseases. Salve combines machine learning with computer-aided design (CAD) to model and simulate the properties and functions of inventions. This approach allows for extensive analysis and optimization of drug candidates before wet lab validation.
Biolexis Therapeutics, another leading player in the field, is utilizing AI to develop oral small molecules to target various diseases, including cancer and metabolic disorders. The company’s AI-based MolecuLern process has been trained on 3D structures and screened against their proprietary data library. This data-driven approach has accelerated the drug discovery process, leading to the development of novel drugs in a matter of months instead of years.
BioSymetrics takes a different approach by focusing on phenomics-driven drug discovery, which aims to reverse disease phenotypes through gene or small-molecule perturbations. The company’s Elion platform integrates vast amounts of clinical and genomic data, machine learning, and experimental phenotypic validation. By prioritizing genes responsible for causing disease phenotypes, BioSymetrics enhances target identification and validation.
These pioneering companies and their use of AI in drug discovery are transforming the field. With the potential to reduce failure rates and accelerate the development of innovative medications, AI is proving to be a game-changer in the pharmaceutical industry. As research and development continues, we can expect to see more AI-designed drugs entering clinical trials and making their way to the market.
The source of the article is from the blog kunsthuisoaleer.nl