AIMC Topic: Machine Learning

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Sensitive Detection and Identification Method of Erythrocyte-like Cells upon Doxorubicin Induced Differentiation with Vibrational Techniques.

Analytical chemistry
Altered differentiation of blood cell precursors and their clonal expansion occurs in various types of leukemia. One treatment strategy is to induce differentiation into the mature form, which is capable of undergoing apoptosis. It has been found tha...

Machine Learning-Assisted Discovery of Bimetallic Oxides for Highly Efficient Catalytic Ozonation.

Environmental science & technology
Catalytic ozonation stands out as an effective process in the advanced treatment of industrial wastewater, where heterogeneous catalysts play a pivotal role. Here, by screening 1603 bimetallic oxides via machine learning (ML), a pioneering ZnCuO was ...

Machine Learning-Assisted Tissue-Residue-Based Risk Assessment for Protecting Threatened and Endangered Fishes in the Yangtze River Basin.

Environmental science & technology
Assessing pollutant risks to threatened and endangered (T&E) species is crucial for their conservation. However, traditional risk assessment methods for bioaccumulative pollutants to T&E fishes is challenging due to uncertainties in exposure-based to...

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials.

Journal of chemical information and modeling
We present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs)...

Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.

Journal of chemical information and modeling
Drug discovery is a lengthy and resource-intensive process that requires innovative computational techniques to expedite the transition from laboratory research to life-saving medications. Here, we introduce Qsarna, a comprehensive online platform th...

log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling.

Journal of chemical information and modeling
Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-b...

Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results.

Journal of chemical information and modeling
Human inspection of potential drug compounds is crucial in the virtual drug screening pipeline. However, there is a pressing need to accelerate this process, as the number of molecules humans can realistically examine is extremely limited relative to...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Journal of chemical information and modeling
Recent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like MEK1, MEK2, PI3Kα, PTEN, SHAP2, and RAS are linked various can...

Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction.

Journal of chemical information and modeling
Electrochemical promotion of catalysis (EPOC) provides an effective and versatile strategy to enhance catalytic activity, selectivity, and stability in the reverse water-gas shift (RWGS) reaction, facilitating efficient CO hydrogenation to syngas und...

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics.

Journal of chemical information and modeling
Molecular dynamics (MD) simulations are vital for exploring complex systems in computational physics and chemistry. While machine learning methods dramatically reduce computational costs relative to ab initio methods, their accuracy in long-lasting s...