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.

De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/

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.

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery.Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, .

Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidates. Conditional 3D structure-based drug design (3D-SBDD) models, which take into account complex three-dimensional interactions and molecular geometries, are particularly promising. Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products. However, the practical application of 3D-SBDD generative models is hampered by their slow processing speeds. To address this bottleneck, we introduce TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model that merges the strategic effectiveness of traditional scaffold hopping with rapid generation capabilities of consistency models. This synergy not only enhances efficiency but also significantly boosts generation speeds, achieving up to 30 times faster inference speed as well as superior generation quality compared to existing diffusion-based models, establishing TurboHopp as a powerful tool in drug discovery.

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 .

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