From AI transformers to computer-based reasoning to rethinking drug design: AI pioneers discuss the future

Jensen Huang at GTC

In a packed panel discussion at GTC, moderated by NVIDIA Founder and CEO Jensen Huang, the architects of the groundbreaking transformer model gathered to explore their creation’s potential. The panel featured seven of the eight authors of the seminal “Attention Is All You Need Paper” paper, which introduced transformers – a type of neural network designed to handle sequential data, like text or time series, in a way that allows for much more parallel processing than previous architectures like recurrent neural networks (RNNs). Transformers accomplish this through a mechanism called “attention,” which enables the model to differentially weigh the importance of different parts of the input data.

The transformer architecture powers large language models like GPT-4 and has ignited widespread interest in AI applications across industries including in biology, wher…

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Microsoft and 1910 Genetics: AI-powered partnership targets billion-dollar savings and growth in drug discovery

[Image: 1910 Genetics]

The pharmaceutical industry is at a critical juncture: AI and other technological advances offer unprecedented potential, yet the cost of developing new drugs has ballooned for decades, surpassing $2 billion in recent years with the projected return on investment (ROI) falling to a mere 1.2% in 2022, according to Deloitte. Another dimension of the problem is the high failure rate — many potential drugs fall short in the expensive clinical trials.

Microsoft and 1910 Genetics have announced a partnership that aims to reverse the troubling trend.

Accelerating discovery with AI and quantum-inspired computing

Microsoft’s Azure Quantum Elements is at the core of this alliance. The platform integrates high-performance computing, AI, and quantum techniques for faster scientific discovery in chemistry and materials science. The goal is to democratize technologies like AI, high-performa…

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Supercomputer-based Bayesian approach to AI pays dividends for BPGbio

In an AI hype-filled biopharma industry, one company is taking a back-to-basics yet supercomputer-powered approach — using Bayesian analysis on massive patient datasets to guide drug discovery. The company crunches trillions of data points per patient. “It’s massive, which is why we use a supercomputer,” said Niven R. Narain, Ph.D., BPGbio CEO. The company has an exclusive relationship with Oak Ridge National Labs, using its Frontier supercomputer to perform complex computational tasks, including the analysis of multi-omics data, the development of predictive models, and the simulation of biological systems. Frontier is hailed as the world’s first exascale supercomputer, meaning it can perform more than 1 quintillion calculations per second.

BPGbio’s AI-powered platform, known as NAi Interrogative Biology, illustrates its approach to drug and diagnostic discovery. The platform includes a lmassive biobank of multi-omi…

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Nvidia-Genentech AI drug discovery alliance unites computing brawn with biological brains

NVIDIA BioNeMo AI molecular modeling software can uncover complex biochemical interactions through AI-driven molecular modeling techniques. [Image courtesy of NVIDIA]

Technically, graphics processing and AI hardware powerhouse Nvidia is also a drug discovery company. It may not discover drugs in-house, but it has developed BioNeMo, a comprehensive generative AI platform for drug discovery and Clara, a collection of healthcare frameworks, applications, and tools, including for biopharma. Nvidia partners include Amgen, AstraZeneca, GSK and Insilico Medicine.

Similarly, biotech pioneer and Roche subsidiary Genentech is also an AI company. It has experience in applying machine learning to an array of disease areas, and has extensive biological and molecular datasets and research capabilities. Its initiatives include alliance with firms such as Recursion Pharmaceuticals and Reverie Labs that focus on using AI for nov…

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How Lantern Pharma and Code Ocean partnered on oncology drug development

A vision for data-driven drug development in oncology

[Adobe Stock]

When Peter Carr, principal software architect of Lantern Pharma, stepped into his full-time role in September 2020, the company was on the cusp of a transformation. While AI had been a focus for a number of years, a fresh infusion of cash provided a possibility of expanding its AI capabilities and machine learning capabilities to drive down the cost of drug development in oncology.

Founded in 2012, the company went public in June 2020, raising $26 million. By the time he officially joined, Carr was already familiar with its operations, having previously worked as a consultant in 2019 to help set up the infrastructure. Carr joined full-time to help the company “expand their use of AI and machine learning for target discovery and patient stratification,” he recalled.

The challenge: Siloed research

While the company had experience in using A…

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Contrastive learning-based model ConPLEx elevates drug-protein interaction predictions

[Generative AI image from Tahsin/Adobe Stock]

Drug discovery, traditionally a labor-intensive process, often involves extensive computational work during experimental screening. Advances in AI, however, promise to streamline this process. To that end, a team from MIT and Tufts has introduced ConPLex, a computational model that uses large language model techniques, similar to those behind ChatGPT. The model analyzes vast amounts of text data to discern patterns and relationships among amino acids. The technique matches potential drug molecules to their target proteins without requiring complex molecular structure computation. The system’s efficiency allows it to sift through an array of more than 100 million compounds in a single day.

Bonnie Berger, head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and one of the senior authors of the new study, ex…

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Q&A: Keys to unlock data science potential for drug discovery

Image courtesy of Pexels

For all of its promise in healthcare and elsewhere, deploying artificial intelligence is frequently a challenging endeavor. “Close collaboration between data science teams, other project team members and stakeholders is essential,” said Jennifer Bradford, director of data science at Phastar, the London-headquartered contract research organization. While input from computational, statistical or medical experts could be essential to inform data science models, all stakeholders understand the requirements and are working “in sync with the project,” Branford said.

In the following interview, Bradford shares advice on how to collaborate effectively on data science projects, the impact of COVID-19 on data science in pharma and the potential for AI to accelerate R&D timelines. 

What comes next after alignment between different stakeholders on data science projects is confirmed? <…

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