The LinkedIn cofounder now has an AI drug discovery startup--and thinks not asking chatbots for medical advice is "bordering on committing malpractice." Following a three-decade career at the helm of some of Silicon Valley's most powerful companies--cofounding LinkedIn and sitting on the boards of PayPal and OpenAI-- Reid Hoffman recently turned his attention to health care. Hoffman's startup, Manas AI, is building an AI engine that aims to fast-track the traditionally slow process of drug discovery for various cancers. Inspired by a dinner with renowned cancer physician Siddhartha Mukherjee, the company's cofounder and CEO, its mission statement is to "shift drug discovery from a decade-long process to one that takes a few years." But Hoffman's enthusiasm for generative AI, in particular, stretches far beyond novel drug targets and small molecules.

Isomorphic Labs president Max Jaderberg said at WIRED Health in London that the startup has built a "broad and exciting pipeline of new medicines." Google DeepMind's AlphaFold has already revolutionized scientists' understanding of proteins . Now, the ability of the platform to design safe and effective drugs is about to be put to the test. Isomorphic Labs, the UK-based biotech spinoff of Google DeepMind, will soon begin human trials of drugs designed by its Nobel Prize-winning AI technology. "We're gearing up to go into the clinic," Isomorphic Labs president Max Jaderberg said on April 16 at WIRED Health in London.

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties.

Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, W elQrate .

Neural Information Processing Systems http://nips.cc/

However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed.

Nevertheless, two issues impede the usage of GNNs in real scenarios: (1)insufficient labeled molecules forsupervised training; (2)poorgeneralization capability to new-synthesized molecules.