TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids

In this work, we introduce , a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. operates with a dual-level strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the effectiveness and capabilities of this program, we have employed to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. has produced a number of conformers that roughly doubles the amount of the most complete work to date. Graphical Abstract

Assignment-free chirality detection in unknown samples via microwave three-wave mixing

Straightforward identification of chiral molecules in multi-component mixtures of unknown composition is extremely challenging. Current spectrometric and chromatographic methods cannot unambiguously identify components while the state of the art spectroscopic methods are limited by the difficult and time-consuming task of spectral assignment. Here, we introduce a highly sensitive generalized version of microwave three-wave mixing that uses broad-spectrum fields to detect chiral molecules in enantiomeric excess without any prior chemical knowledge of the sample. This method does not require spectral assignment as a necessary step to extract information out of a spectrum. We demonstrate our method by recording three-wave mixing spectra of multi-component samples that provide direct evidence of enantiomeric excess. Our method opens up new capabilities in ultrasensitive phase-coherent spectroscopic detection that can be applied for chiral detection in real-life mixtures, raw products of chemical reactions and difficult to assign novel exotic species.

Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

The Making of Evident Expertise: Transforming Chemical Analytical Methods into Judicial Evidence

This article investigates the question of how forensic toxicologists established the credibility of chemical analytical methods in poisoning lawsuits in the nineteenth century. After encountering the problem of laypersons in court, forensic toxicologists attempted to find strategies to make their evidence compelling to an untrained audience. Three of these strategies are discussed here: redundancy, standard methods, and intuitive comprehensibility. Whereas redundancy was not very practical and legally prescribed standard methods were not very popular with most forensic toxicologists, intuitive comprehensibility proved effective and popular. This strategy relied on employing methods which did not require chemical knowledge to be understandable. The methods aimed to generate a visual aid and to be obvious in their results. Two forms of this strategy are discussed here: the presentation of the actual material and explicit comparison. I argue that this shift towards presenting forensic toxicology expertise as evident represents an important step in the history of forensic expertise.

IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

Group Contribution Revisited: The Enthalpy of Formation of Organic Compounds with “Chemical Accuracy”

Group contribution (GC) methods to predict thermochemical properties are of eminent importance to process design. Compared to previous works, we present an improved group contribution parametrization for the heat of formation of organic molecules exhibiting chemical accuracy, i.e., a maximum 1 kcal/mol (4.2 kJ/mol) difference between the experiment and model, while, at the same time, minimizing the number of parameters. The latter is extremely important as too many parameters lead to overfitting and, therewith, to more or less serious incorrect predictions for molecules that were not within the data set used for parametrization. Moreover, it was found to be important to explicitly account for common chemical knowledge, e.g., geminal effects or ring strain. The group-related parameters were determined step-wise: first, alkanes only, and then only one additional group in the next class of molecules. This ensures unique and optimal parameter values for each chemical group. All data will be made available, enabling other researchers to extend the set to other classes of molecules.