1,425 research outputs found
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Predicting drug metabolism: experiment and/or computation?
Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458
A generative model for electron paths
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using “arrow-pushing” diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants. We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.EPSR
Machine learning activation energies of chemical reactions
Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable
combinations of chemical elements and (meta-)stable geometries that make up
matter, is colossal. The first principles based virtual sampling of this space,
for example in search of novel molecules or materials which exhibit desirable
properties, is therefore prohibitive for all but the smallest sub-sets and
simplest properties. We review studies aimed at tackling this challenge using
modern machine learning techniques based on (i) synthetic data, typically
generated using quantum mechanics based methods, and (ii) model architectures
inspired by quantum mechanics. Such Quantum mechanics based Machine Learning
(QML) approaches combine the numerical efficiency of statistical surrogate
models with an {\em ab initio} view on matter. They rigorously reflect the
underlying physics in order to reach universality and transferability across
CCS. While state-of-the-art approximations to quantum problems impose severe
computational bottlenecks, recent QML based developments indicate the
possibility of substantial acceleration without sacrificing the predictive
power of quantum mechanics
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Deep learning-based reaction predictors have undergone significant
architectural evolution. However, their reliance on reactions from the US
Patent Office results in a lack of interpretable predictions and limited
generalization capability to other chemistry domains, such as radical and
atmospheric chemistry. To address these challenges, we introduce a new reaction
predictor system, RMechRP, that leverages contrastive learning in conjunction
with mechanistic pathways, the most interpretable representation of chemical
reactions. Specifically designed for radical reactions, RMechRP provides
different levels of interpretation of chemical reactions. We develop and train
multiple deep-learning models using RMechDB, a public database of radical
reactions, to establish the first benchmark for predicting radical reactions.
Our results demonstrate the effectiveness of RMechRP in providing accurate and
interpretable predictions of radical reactions, and its potential for various
applications in atmospheric chemistry
Where is your field going? A Machine Learning approach to study the relative motion of the domains of Physics
We propose an original approach to describe the scientific progress in a
quantitative way. Using innovative Machine Learning techniques we create a
vector representation for the PACS codes and we use them to represent the
relative movements of the various domains of Physics in a multi-dimensional
space. This methodology unveils about 25 years of scientific trends, enables us
to predict innovative couplings of fields, and illustrates how Nobel Prize
papers and APS milestones drive the future convergence of previously unrelated
fields
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