Nonequilibrium sensing of volatile compounds using active and passive analyte delivery.

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Vibrational Raman Shifts of Spin Isomer Combinations of Hydrogen Dimers and Isotopologues.

Binding energies for para-para, ortho-para, and ortho-ortho hydrogen dimers (H2)2 are calculated using the six-dimensional (6D) interaction potential developed by Hinde [ J. Chem. Phys. 2008, 128, 154308]. The eigenstates of the dimers are computed by diagonalization using, as a basis, products of the rovibrational states of the monomers, a radial grid for the distance between the monomers, and spherical harmonics for the end-over-end rotation of the dimer. We describe the overall nuclear spin symmetry and use these properties to determine the relative population of various states, making use of a Boltzmann factor for each spin isomer to assess the effect of temperature. A predicted Raman spectrum in the Q(0) and Q(1) region of the hydrogen dimer is produced. To assess the accuracy of our model, we verify our produced shifts with experimental results obtained previously by Montero et al. [ Eur. Phys. J. D 2009, 52, 31-34] and find good agreement. These results are extended to other cases involving the deuterium (D2)2 and tritium dimer (T2)2 isotopologues, to predict Raman shifts.

Petroleomic analysis of the treatment of naphthenic organics in oil sands process-affected water with buoyant photocatalysts.

The persistence of toxicity associated with the soluble naphthenic organic compounds (NOCs) of oil sands process-affected water (OSPW) implies that a treatment solution may be necessary to enable safe return of this water to the environment. Due to recent advances in high-resolution mass spectrometry (HRMS), the majority of the toxicity of OSPW is currently understood to derive from a subset of toxic classes, comprising only a minority of the total NOCs. Herein, oxidative treatment of OSPW with buoyant photocatalysts was evaluated under a petroleomics paradigm: chemical changes across acid-, base- and neutral-extractable organic fractions were tracked throughout the treatment with both positive and negative ion mode electrospray ionization (ESI) Orbitrap MS. Elimination of detected OS+ and NO+ classes of concern in the earliest stages of the treatment, along with preferential degradation of high carbon-numbered O2- acids, suggest that photocatalysis may detoxify OSPW with higher efficiency than previously thought. Application of petroleomic level analysis offers unprecedented insights into the treatment of petroleum impacted water, allowing reaction trends to be followed across multiple fractions and thousands of compounds simultaneously.