IBM bets on AI and robotics to speed up drug discovery

So you’re interested in AI? Then join our online event, TNW2020, where you’ll hear how artificial intelligence is transforming industries and businesses.

An end-to-end, integrated chemical research system unveiled by IBM last week gives us a glimpse of how artificial intelligence, robotics and the cloud might change the future of drug discovery.

And it’s a good time as any to see some a breakthrough in the field.

The world is still struggling with the COVID-19 pandemic, and the race to the find a vaccine for the dangerous novel coronavirus has not yet yielded reliable results. Researchers are bound by travel and social distancing limitations imposed by the virus, and for the most part, they still rely on manual methods that can take many years. While in some cases, such delays can result in inconvenience, in the case of COVID-19, it means more lives lost.

Called RoboRXN, IBM’s new system leverages deep learning algorithms, IBM’s cloud, and robotic labs to automate the entire process and assist chemists in their work without requiring physical presence in a research lab. After seeing the presentation by IBM Research, I would describe RoboRXN as an example of bringing together the right pieces to solve a pressing problem.

It’s not yet clear whether this or any of the other efforts led by other large tech companies will help facilitate in developing the coronavirus vaccine. But they will surely help lay the groundwork for the next generation of drug and chemicals research tools and make sure we are more prepared in the future.

Using AI for chemical synthesis and retrosynthesis

IBM’s RoboRXN is the culmination of three years of research and development in applying AI to chemical research. In 2017, the company developed an AI system for predicting chemical reactions in forward synthesis.

Hypothesizing about chemical reactions and experimenting with different chemical components is one of the most time-consuming parts of chemical research. It requires a lot of experience, and chemists usually specialized in specific fields, making it challenging for them to tackle new tasks.

IBM’s AI is a neural machine translation system tailored to chemical synthesis. Artificial neural networks have made great inroads in natural language processing in recent years. While neural networks do not understand the context of human language, their broader capabilities in processing sequential data can serve many fields, including chemical research.

For instance, recurrent neural networks (RNN) and transformers can perform sequence-to-sequence mapping. Train an RNN on a set of input strings and their corresponding output strings, and it will find statistical correlations that map the inputs to outputs (you still need quality data, though). These strings can contain any kind of symbols, including letters, musical notes, or character representations of atoms and molecules. As long as there is consistency in the data and there are patterns to be learned, the neural network can find a way to map the inputs to the outputs.

Trained on a dataset of more than 2 million chemical reactions, the neural network was first introduced in a paper presented by the IBM Research team at the NIPS 2017 AI conference. The next year, IBM developed the AI into RXN for Chemistry, a cloud-based platform for chemical research, and presented it at the American Chemical Society annual exposition. RXN for Chemistry aids chemists in predicting the likely outcome of chemical reactions, saving research time, and reducing the years it takes to acquire experience.

In 2019, the IBM Research team improved the AI behind RXN for Chemistry to also support retrosynthesis. This is the inverse process of chemical synthesis. In this case, you already know the molecular structure you want to achieve. The AI must predict the series of steps and chemical components needed to reach the desired result.

“The retrosynthesis planning model models were developed in collaboration with retrosynthesis experts from the University of Pisa, who constantly gave us feedback how to improve our models,” Teodoro Laino, the manager of IBM Research Zurich, told TechTalks.

IBM RXN for Chemistry also has the possibility to design retrosynthetic routes in an interactive mode.

In the interactive mode, the human chemist goes through the route step by step, getting suggestions by the AI at each stage. “Chemical synthesis becomes a human-AI interaction game,” Laino says.

Bringing the AI pieces together

Philippe Schwaller, predoctoral researcher at IBM Research Zurich, told TechTalks that the final AI system used in RoboRXN is composed of several sequence-to-sequence transformer models, each performing one part of the task.

“Given a target molecule, RoboRXN breaks it down in multiple recipe steps using predictions by a retro reaction prediction and a pathway scoring model until the system finds commercially available molecules,” Schwaller said. “Then, for each step in the recipe, the reaction equations are converted using another seq-2-seq transformer model to all necessary actions, which the robot has to perform, to successfully run the chemical reaction. This model predicts reaction conditions (e.g. temperature, duration) for the different actions (e.g. add, stir, filter).”

In the process of creating the AI, the team published their findings in several peer-reviewed journals and made their AI models available on a GitHub repository. Their latest paper, published in Nature in July, explores the use of transformers to translate the chemical experiments written in open-prose to distinct steps. This is a key component in integrating the AI system with robo-labs, which expect distinct commands.

“For a given target molecule, RoboRXN provides not only a recipe made of multiple chemical reactions that would lead from commercially available molecules to the target molecule, but is also able to generate for each step in the recipe, the specific actions that a robot or human has to perform to successfully run the reaction step,” Laino says.

Theodoro Laino IBM Research RoboRXN