Supercomputers On The Hunt For Middle Molecules


Called the Program to Industrialize an Innovative Middle Molecule Drug Discovery Flow through Fusion of Computational Drug Design and Chemical Synthesis Technology, the partnership will see both sides jointly conduct R&D and construct infrastructure. Conventional drug discovery mainly consists of synthesizing small molecules with a molecular weight under 500 daltons (Da), a strategy known as small molecule drug discovery. In recent years, drug discovery has turned to large molecules such as antibodies that have therapeutic properties. However, industrial synthesis of large molecules is difficult and poses numerous issues such as the extremely high cost incurred for creation using animal cells under advanced control conditions. On the other hand, middle molecules--peptides, nucleic acids and other molecules with a molecular weight of about 500 to 30,000 Da--can be chemically synthesized and may offer benefits that are similar to large molecules.

Novel Molecules Designed by Artificial Intelligence May Accelerate Drug Discovery


Deep Learning enables rapid identification of potent DDR1 Kinase Inhibitors. Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

A breakthrough in imaginative AI with experimental validation to accelerate drug discovery


The many advances in deep learning reinforcement learning and generative adversarial learning made since 2014 are rapidly transforming multiple industries including search, translation, video games, retail, transportation, and many others. It is relatively easy to validate the performance of the AI systems in imaging, voice, text and other areas where human sensory systems can be used to rapidly verify the validity of the experimental results. However, in the pharmaceutical industry, the validation cycles take decades and cost billions of dollars. Most of the common questions asked by the pharmaceutical industry executives to all of the leading artificial intelligence groups worldwide deal with the novelty of the algorithms and experimental validation of the results in mice or even in humans. There is a grave disconnect between the leaders in AI focusing on the novelty of the algorithms and drug discovery and development experts focusing only on experimental data.

The importance of synthetic chemistry in the pharmaceutical industry


Over the past century, innovations in synthetic methods have changed the way scientists think about designing and building molecules, enabling access to more expansive chemical space and to molecules possessing the essential biological activity needed in future investigational drugs. In order for the pharmaceutical industry to continue to produce breakthrough therapies that address global health needs, there remains a critical need for invention of synthetic transformations that can continue to drive new drug discovery. Toward this end, investment in research directed toward synthetic methods innovation, furthering the nexus of synthetic chemistry and biomolecules, and developing new technologies to accelerate methods discovery is essential. One powerful example of an emerging, transformative synthetic method is the recent discovery of photoredox catalysis, which allows one to harness the energy of visible light to accomplish synthetic transformations on drug-like molecules that were previously unachievable. Furthermore, recent breakthroughs in molecular biology, bioinformatics, and protein engineering are driving rapid identification of biocatalysts that possess desirable stability, unique activity, and exquisite selectivity needed to accelerate drug discovery.

Artificial intelligence in the legal industry: Adoption and strategy - Part 1


Following on from a report that identified artificial intelligence (AI) as crucial in overcoming challenges in the legal industry, Information Age wanted to delve deeper into the subject. As a result, we got in touch Geoffrey Vance, the chair of Perkins Coie's E-Discovery Services and Strategy Practice, and Alvin Lindsay, partner at Hogan Lovells. Vance took over the Perkins Coie practice 3 years ago after leading McDermott Will & Emery's e-discovery group for 7 years, and has led Perkins Coie to be one of the first to use AI for various discovery, litigation and other legal needs. He's seen associates get more involved in this kind of work right at the start of their careers, and spoke to Information Age about the evolution of their roles with the rise of AI. At the same time, Lindsay has written about potential of AI in the legal industry and is well placed to comment on this encroaching trend.