AIMC Topic: Chemistry, Organic

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CTsynther: Contrastive Transformer Model for End-to-End Retrosynthesis Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Retrosynthesis prediction is a fundamental problem in organic chemistry and drug synthesis. We proposed an end-to-end deep learning model called CTsynther (Contrastive Transformer for single-step retrosynthesis prediction model) that could provide si...
Computational Biology Chemistry, Organic Algorithms Deep Learning
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Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning.

Small (Weinheim an der Bergstrasse, Germany) Feb 11, 2023
Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicit...
Nanostructures Chemistry, Organic Computer Systems Machine Learning Artificial Intelligence
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Machine-Learning-Guided Discovery of Electrochemical Reactions.

Journal of the American Chemical Society Dec 2, 2022
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all dis...
Chemistry, Organic Machine Learning Molecular Structure
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Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization.

The Journal of organic chemistry Jul 8, 2021
We combine random sampling and active machine learning (ML) to optimize the synthesis of isomacroin, executing only 3% of all possible Friedländer reactions. Employing kinetic modeling, we augment machine intuition by extracting mechanistic knowledge...
Machine Learning Kinetics Chemistry, Organic
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Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables.

Journal of chemical information and modeling Jan 7, 2021
Retrosynthesis is an essential task in organic chemistry for identifying the synthesis pathways of newly discovered materials, and with the recent advances in deep learning, there have been growing attempts to solve the retrosynthesis problem through...
Chemistry, Organic Neural Networks, Computer
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Computational planning of the synthesis of complex natural products.

Nature Oct 13, 2020
Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years. However, the field has progressed greatly since the development of early programs such as LHASA, for which reaction choices at each s...
Chemistry, Organic Benzylisoquinolines Indole Alkaloids Indans Tetrahydroisoquinolines Chemistry Techniques, Synthetic Automation Biological Products Macrolides Sesquiterpenes Reproducibility of Results Lactones Artificial Intelligence Knowledge Bases Software
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Expert system for predicting reaction conditions: the Michael reaction case.

Journal of chemical information and modeling Feb 3, 2015
A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may ...
Catalysis Models, Chemical Bayes Theorem Reproducibility of Results Chemistry, Organic Quantitative Structure-Activity Relationship Predictive Value of Tests Indicators and Reagents Informatics Support Vector Machine Expert Systems Machine Learning Databases, Factual Algorithms
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Planning chemical syntheses with deep neural networks and symbolic AI.

Nature Mar 28, 2018
To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable t...
Artificial Intelligence Chemistry Techniques, Synthetic Monte Carlo Method Neural Networks, Computer Chemistry, Organic
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