No More Guesswork: Predicting Reactions e Robustness with AI

Hey there! Ever felt like predicting whether a chemical reaction will actually work, or if it’s going to be a total diva and refuse to cooperate when you scale it up, is a bit of a black art? Yeah, me too. For ages, organic chemists have dreamed of having some kind of “oracle” – a crystal ball that could tell you if a reaction is a go or a no-go before you even step into the lab. Think of the time and money saved, especially in places like drug discovery where every second and every penny counts!

Trouble is, chemistry is complex. While we’ve made huge strides in understanding *why* things react the way they do, predicting the outcome of *any* given reaction just from first principles is still super tough. And while some smart folks are using AI on existing data, they often run into a big problem: published data is usually all about the successes. We rarely see the “oops, that didn’t work” results, which are crucial for training a truly smart system.

So, predicting feasibility relies heavily on the intuition of seasoned experts. And let’s be honest, training those experts takes time, effort, and a whole lot of failed experiments along the way. Building an AI that can match that expertise? Even harder! It needs a smart way to explore the vast chemical universe and get its hands on unbiased data, automatically.

But wait, there’s more! Even if a reaction *is* feasible, is it robust? Will it work reliably every single time, even with tiny changes in moisture, oxygen, light, or just a slightly different way someone stirs the pot? Some reactions are incredibly sensitive, making them a nightmare to reproduce, let alone scale up for industrial production. Process engineers are constantly looking for less finicky alternatives. Predicting this “robustness” is another massive challenge.

Why is it so hard? Well, first, you need a way to explore a huge range of chemical possibilities quickly and automatically. Second, you need to understand the *uncertainty* behind the results – not just whether it worked, but *how sure* you are about that result, and *why* there might be variability. Until now, systematically digging into this intrinsic “stochasticity” (fancy word for randomness or variability) of chemical reactions has been pretty much impossible.

Our Solution: A Dynamic Duo

Alright, enough with the problems! We decided to tackle this head-on with a powerful combination: High Throughput Experimentation (HTE) and Bayesian Deep Learning. Our goal? To create a systematic way to predict both reaction feasibility *and* robustness.

Here’s the game plan:

• Build a massive, unbiased dataset using an automated HTE platform.
• Develop a smart learning strategy using a Bayesian neural network (BNN) that uses uncertainty to guide exploration and predict feasibility with minimal data.
• Analyze the uncertainty to understand the intrinsic variability of reactions and estimate robustness.

We focused on acid-amine coupling reactions. Why? Because they’re everywhere in organic synthesis, but still tricky even for experienced chemists. We set up our in-house HTE platform to explore a broad range of acids, amines, reagents, bases, and solvents.

Building the Data Mountain: High Throughput Experimentation

Get this: our HTE platform ran 11,669 distinct acid-amine coupling reactions in just 156 working hours! That’s insane speed compared to traditional methods. This resulted in the most extensive single HTE dataset for this reaction type at a volume scale (200-300 µL) that’s actually practical for industrial use. It also covers the most target products seen in a dataset like this.

We didn’t just randomly pick reactions. We carefully curated our chemical space to resemble reactions found in patents, but used commercially available compounds to make it feasible. We even designed experiments to include potentially negative results, based on known chemical principles like steric hindrance. This helps combat the “positive result bias” found in literature data.

Our dataset is massive and covers a broad substrate space, unlike many HTE studies that focus on optimizing conditions for a narrow set of reactants. This broadness is key for training a model that can predict feasibility across a wide range of reactions, not just a specific niche.

Interestingly, even with all our chemical knowledge, the HTE sometimes threw us curveballs – reactions we expected to fail due to factors like steric hindrance or partial charges actually gave pretty good yields. This just shows you can’t beat real experimental data!

Bringing in the Brains: Bayesian Deep Learning

Predicting feasibility isn’t just a yes/no question; it’s about probability and confidence. That’s where our Bayesian Neural Network (BNN) comes in. Unlike standard models that give you a single prediction, our BNN gives us a probability distribution. This means it tells us not just *what* it predicts, but *how sure* it is about that prediction.

We compared our BNN model (specifically, one using a technique called NUTS for training) against other methods on both our huge acid-amine dataset and a public dataset. The results were clear: our BNN+NUTS model consistently outperformed the others. On our dataset, it achieved a benchmark accuracy of 89.48% for reaction feasibility prediction in the most common scenario (random data split). Even when we tested it on reactions where one or both reactants were completely “unseen” during training (a much harder test!), it still performed remarkably well, showing its ability to generalize.

Beyond just accuracy, our BNN is also well-calibrated. This means that when the model says a reaction has, say, an 80% chance of working, it actually works about 80% of the time in experiments. This is crucial for trusting the model’s predictions and its confidence levels.

Understanding ‘Maybe’: Disentangling Uncertainty

One of the coolest things we did was disentangle the uncertainty into two types:

• Epistemic Uncertainty: This is the model’s “I don’t know enough about this” uncertainty. It comes from areas where the model hasn’t seen much data.
• Aleatoric Uncertainty: This is the data’s inherent “it’s just noisy” uncertainty. It comes from the fact that even with perfect knowledge, the reaction itself might be sensitive to tiny, uncontrollable factors.

Why separate them? Because they tell us different things! Epistemic uncertainty is a signal for active learning. If the model is unsure because it lacks data in a certain area, we know exactly which experiments to run next to improve its knowledge most efficiently. We showed that using this strategy, we could achieve similar prediction performance with about 80% less data compared to just picking random experiments! That’s a huge time and resource saver.

Aleatoric uncertainty, on the other hand, seems to be a great indicator of reaction robustness. If a reaction has high aleatoric uncertainty, it suggests the outcome is inherently variable, likely sensitive to subtle environmental factors or prone to side reactions. This makes it harder to reproduce reliably.

From Lab Bench to Factory Floor: Validating Robustness

To test our theory about aleatoric uncertainty and robustness, we did something neat. We repeated a subset of reactions from our HTE dataset three times. We picked reactions with high aleatoric uncertainty, low aleatoric uncertainty, and some in between. The results were striking: reactions with high aleatoric uncertainty showed poor reproducibility, with yields jumping all over the place. Reactions with low aleatoric uncertainty were super consistent, almost perfectly reproducible!

We also looked at real-world industrial data from the literature. We compared data from the “discovery phase” (small-scale, mg-level reactions often less optimized) with data from the “process phase” (large-scale, kg/ton-level reactions that have been heavily optimized for robustness). Guess what? Reactions from the process phase had significantly lower aleatoric uncertainty, even when we accounted for the difference in data volume. This strongly supports the idea that aleatoric uncertainty is indeed a good predictor of how robust a reaction will be when you try to scale it up.

We even saw this in a specific example from the pharmaceutical industry – the production of the drug Bortezomib. Our model correctly identified that the reaction used in the optimized process phase had lower aleatoric uncertainty than the one used in the earlier discovery phase, suggesting it was more robust.

Wrapping It Up: A New Era for Synthesis?

So, what does this all mean? We’ve built a framework that combines the power of automated experimentation with smart AI that understands uncertainty. We created the largest dataset of its kind for acid-amine coupling, including crucial negative results. Our model can predict reaction feasibility with high accuracy and, crucially, estimate how robust a reaction is likely to be.

By disentangling uncertainty, we can now intelligently decide which experiments to run next to learn the most, saving tons of time and resources. We can also flag reactions that are likely to be headaches during scale-up, allowing chemists and engineers to find more reliable alternatives early on.

This feels like a big step towards a future where chemists have powerful AI tools to help them navigate the vast chemical space more efficiently, design better synthesis routes, and develop robust industrial processes. We’re excited to keep exploring this approach for other reaction types and move closer to that dream of a universal reaction prediction oracle!

Source: Springer