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A robotic prebiotic chemist probes long term reactions of complexifying mixtures

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To experimentally test hypotheses about the emergence of living systems from abiotic chemistry, researchers need to be able to run intelligent, automated, and long-term experiments to explore chemical space. Here we report a robotic prebiotic chemist equipped with an automatic sensor system designed for long-term chemical experiments exploring unconstrained multicomponent reactions, which can run autonomously over long periods. The system collects mass spectrometry data from over 10 experiments, with 60 to 150 algorithmically controlled cycles per experiment, running continuously for over 4 weeks. We show that the robot can discover the production of high complexity molecules from simple precursors, as well as deal with the vast amount of data produced by a recursive and unconstrained experiment. This approach represents what we believe to be a necessary step towards the design of new types of Origin of Life experiments that allow testable hypotheses for the emergence of life from prebiotic chemistry. The transition of prebiotic chemistry to present-day chemistry lasted a very long period of time, but the current laboratory investigations of this process are mostly limited to a couple of days. Here, the authors develop a fully automated robotic prebiotic chemist designed for long-term chemical experiments exploring unconstrained multicomponent reactions, which can run autonomously and uses simple chemical inputs.


Tiny particles power chemical reactions

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MIT engineers have discovered a new way of generating electricity using tiny carbon particles that can create a current simply by interacting with liquid surrounding them. The liquid, an organic solvent, draws electrons out of the particles, generating a current that could be used to drive chemical reactions or to power micro- or nanoscale robots, the researchers say. "This mechanism is new, and this way of generating energy is completely new," says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT. "This technology is intriguing because all you have to do is flow a solvent through a bed of these particles. This allows you to do electrochemistry, but with no wires." In a new study describing this phenomenon, the researchers showed that they could use this electric current to drive a reaction known as alcohol oxidation -- an organic chemical reaction that is important in the chemical industry.


To speed discoveries, U of T lab launches free library of virtual, AI-calculated organic compounds

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Alán Aspuru-Guzik's research group at the University of Toronto has launched an open-access tool that promises to accelerate the discovery of new chemical reactions that underpin the development of everything from smartphones to life-saving drugs. The free tool, called Kraken, is a library of virtual, machine-learning calculated organic compounds – roughly 300,000 of them, with 190 descriptors each. It was created through a collaboration between Aspuru-Guzik's Matter Lab, the Sigman Research Group at the University of Utah, Technische Universität Berlin, Karlsruhe Institute of Technology, Vector Institute for Artificial Intelligence, the Center for Computer Assisted Synthesis at the University of Notre Dame, IBM Research and AstraZeneca "The world has no time for science as usual," says Aspuru-Guzik, a professor in U of T's departments of chemistry and computer science in the Faculty of Arts & Science. "Neither for science done in a silo. "This is a collaborative effort to accelerate catalysis science that involves a very exciting team from academia and industry." When developing a transition-metal catalyzed chemical reaction, a chemist must find a suitable combination of metal and ligand. Despite the innovations in computer-optimized ligand design led by the Sigman group, ligands would typically be identified by trial and error in the lab. With Kraken, however, chemists will eventually have a vast data-rich collection at their fingertips, reducing the number of trials necessary to achieve optimal results. "It takes a long time, a lot of money, and a whole lot of human resources to discover, develop and understand new catalysts and chemical reactions." "These are some of the tools that allow molecular scientists to precisely develop materials and drugs, from the plastics in your smartphone to the probes that allowed for humanity to achieve the COVID-19 vaccines at an unforeseen pace.


Machine learning made easy for optimizing chemical reactions

Nature

The optimization of reactions used to synthesize target compounds is pivotal to chemical research and discovery, whether in developing a route for manufacturing a life-saving medicine1 or unlocking the potential of a new material2. But reaction optimization requires iterative experiments to balance the often conflicting effects of numerous coupled variables, and frequently involves finding the sweet spot among thousands of possible sets of experimental conditions. Expert synthetic chemists currently navigate this expansive experimental void using simplified model reactions, heuristic approaches and intuition derived from observation of experimental data3. Writing in Nature, Shields et al.4 report machine-learning software that can optimize diverse classes of reaction with fewer iterations, on average, than are needed by humans. Machine learning has emerged as a useful tool for various aspects of chemical synthesis, because it is ideally suited to extrapolating predictive models that are used to solve synthetic problems by recognizing patterns in multidimensional data sets5.


Prediction of drug metabolites using deep learning

AIHub

By Mike Williams When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. With a new deep learning-based technique created at Rice University's Brown School of Engineering, they may soon get a better handle on how drugs in development will perform in the human body. Lydia Kavraki, Professor of Computer Science, has introduced Metabolite Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes. The Rice researchers take advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.


What is Reinforcement Learning and 9 examples of what you can do with it.

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Reinforcement Learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example. Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result. It differs from other forms of supervised learning because the sample data set does not train the machine.


Deep learning gives drug design a boost

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When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. With a new deep learning-based technique created at Rice University's Brown School of Engineering, they may soon get a better handle on how drugs in development will perform in the human body. The Rice lab of computer scientist Lydia Kavraki has introduced Metabolite Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes. The Rice researchers take advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.


Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants

arXiv.org Machine Learning

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. With the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our models using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.


AI tool could predict how drugs will react in the body - Futurity

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You are free to share this article under the Attribution 4.0 International license. A new deep learning-based tool called Metabolic Translator may soon give researchers a better handle on how drugs in development will perform in the human body. When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. Metabolic Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes could help improve the process. The new tool takes advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.


New deep learning-based technique could boost drug development

#artificialintelligence

When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. With a new deep learning-based technique created at Rice University's Brown School of Engineering, they may soon get a better handle on how drugs in development will perform in the human body. The Rice lab of computer scientist Lydia Kavraki has introduced Metabolite Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes. The Rice researchers take advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.