Collaborating Authors


A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling


Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning.

Using computational tools for molecule discovery


Discovering a drug, material, or anything new requires finding and understanding molecules. It's a time- and labor-intensive process, which can be helped along by a chemist's expertise, but it can only go so quickly, be so efficient, and there's no guarantee for success. Connor Coley is looking to change that dynamic. The Henri Slezynger (1957) Career Development Assistant Professor in the MIT Department of Chemical Engineering is developing computational tools that would be able to predict molecular behavior and learn from the successes and mistakes. It's an intuitive approach and one that still has obstacles, but Coley says that this autonomous platform holds enormous potential for remaking the discovery process.

Machine-learning software competes with human experts to optimise organic reactions


A free software tool that can find the best conditions for organic synthesis reactions often does as well as expert chemists – somewhat to the surprise of the researchers. The software, called LabMate.ML, suggests a random set of initial conditions – such as the temperature, the amount of solvent and the reaction time – for a specific reaction, with the aim of optimising its yield. After those initial reactions are carried out by a human chemist, their resulting yields are read with nuclear magnetic resonance and infrared spectroscopy, digitised into binary code and then fed back into the software. LabMate.ML then uses a machine-learning algorithm to make decisions about the yields, and then recommends further sets of conditions to try. Researcher Tiago Rodrigues of the University of Lisbon says LabMate.ML usually takes between 10 and 20 iterations to find the greatest yield, while the number of initial reactions varies between five and 10, depending on how many conditions are being optimised.

Gini in a Bottleneck: Gotta Train Me the Right Way Machine Learning

Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network. As a mitigation strategy, we constrain certain weights in a multi-task graph convolutional neural network according to the Gini index to maximize the "inequality" of the learned representations. We show that this constraint does not degrade evaluation metrics for some targets, and allows us to combine the outputs of the graph convolutional operation in a visually interpretable way. We then perform a proof-of-concept experiment on quantum chemistry targets on the public QM9 dataset, and a larger experiment on ADMET targets on proprietary drug-like molecules. Since a benchmark of explainability in the latter case is difficult, we informally surveyed medicinal chemists within our organization to check for agreement between regions of the molecule they and the model identified as relevant to the properties in question.

How artificial intelligence and robotics are changing chemical research


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.

The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry Machine Learning

The space of synthesizable molecules is greater than $10^{60}$, meaning only a vanishingly small fraction of these molecules have ever been realized in the lab. In order to prioritize which regions of this space to explore next, synthetic chemists need access to accurate molecular property predictions. While great advances in molecular machine learning have been made, there is a dearth of benchmarks featuring properties that are useful for the synthetic chemist. Focussing directly on the needs of the synthetic chemist, we introduce the Photoswitch Dataset, a new benchmark for molecular machine learning where improvements in model performance can be immediately observed in the throughput of promising molecules synthesized in the lab. Photoswitches are a versatile class of molecule for medical and renewable energy applications where a molecule's efficacy is governed by its electronic transition wavelengths. We demonstrate superior performance in predicting these wavelengths compared to both time-dependent density functional theory (TD-DFT), the incumbent first principles quantum mechanical approach, as well as a panel of human experts. Our baseline models are currently being deployed in the lab as part of the decision process for candidate synthesis. It is our hope that this benchmark can drive real discoveries in photoswitch chemistry and that future benchmarks can be introduced to pivot learning algorithm development to benefit more expansive areas of synthetic chemistry.

A Bayesian algorithm for retrosynthesis Machine Learning

The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space. At present, emerging machine-learning technologies are overturning the process of retrosynthetic planning. The objective of this study is to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of two steps: a deep neural network is trained that forwardly predicts a product of the given reactants with a high level of accuracy, following which this forward model is inverted into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. The Bayesian retrosynthesis algorithm could successfully rediscover 80.3% and 50.0% of known synthetic routes of single-step and two-step reactions within top-10 accuracy, respectively, thereby outperforming state-of-the-art algorithms in terms of the overall accuracy. Remarkably, the Monte Carlo method, which was specifically designed for the presence of diverse multiple routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We investigated the potential applicability of such diverse candidates based on expert knowledge from synthetic organic chemistry.

Artificial Intelligence (AI) and medicine


Chris Smith and Phil Sansom delve into the world of artificial Intelligence (AI) to find out how this emerging technology is changing the way we practise medicine... Mike - I think this is an area where AI stands a really good chance of making quite dramatic improvements to very large numbers of people's lives. Carolyn - Save lives and reduce medical complications. Beth - That's a concern - when machine-learning algorithms learn the wrong things. Andrew - Frankly revolutionary productivity that we are now starting to see from these AI approaches in drug design. Lee - It will replace all manual labor in all research laboratories. And then suddenly everyone can collaborate. Phil - But what was previously sci-fi is now closer to reality. AI technology exists, and there's a brand new frontier where it's being applied to the world of healthcare. Chris - But this isn't the AI you see in the movies.

Chemists are training machine learning algorithms used by Facebook and Google to find new molecules


For more than a decade, Facebook and Google algorithms have been learning as much as they can about you. It's how they refine their systems to deliver the news you read, those puppy videos you love, and the political ads you engage with. These same kinds of algorithms can be used to find billions of molecules and catalyze important chemical reactions that are currently induced with expensive and toxic metals, says Steven A. Lopez, an assistant professor of chemistry and chemical biology at Northeastern. Lopez is working with a team of researchers to train machine learning algorithms to spot the molecular patterns that could help find new molecules in bulk, and fast. "We're teaching the machines to learn the chemistry knowledge that we have," Lopez says.

Guided by AI, robotic platform automates molecule manufacture


Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules that could be used in medicine, solar energy, and polymer chemistry. The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT. The technology "has the promise to help people cut out all the tedious parts of molecule building," including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen. "And as a chemist, it may give you inspirations for new reactions that you hadn't thought about before," he adds. The new system combines three main steps.