Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels.

Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a dataset of 382 molecules with toxicity labels from honey bee exposure experiments, we train a support vector machine (SVM) to predict the toxicity of pesticides to honey bees. We compare two representations of the pesticide molecules: (i) a random walk feature vector listing counts of length-L walks on the molecular graph with each vertex- and edge-label sequence and (ii) the Molecular ACCess System (MACCS) structural key fingerprint (FP), a bit vector indicating the presence/absence of a list of pre-defined subgraph patterns in the molecular graph. We explicitly construct the MACCS FPs but rely on the fixed-length-L random walk graph kernel (RWGK) in place of the dot product for the random walk representation. The L-RWGK-SVM achieves an accuracy, precision, recall, and F1 score (mean over 2000 runs) of 0.81, 0.68, 0.71, and 0.69, respectively, on the test data set-with L = 4 being the mode optimal walk length. The MACCS-FP-SVM performs on par/marginally better than the L-RWGK-SVM, lends more interpretability, but varies more in performance. We interpret the MACCS-FP-SVM by illuminating which subgraph patterns in the molecules tend to strongly push them toward the toxic/non-toxic side of the separating hyperplane.

PoreMatMod.jl: Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models.

PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for subgraphs of the crystal structure graph that are isomorphic to the graph of a query fragment and (ii) the orthogonal Procrustes algorithm to align a replacement fragment with a targeted substructure of the crystal structure for installation. The prominent application of PoreMatMod.jl is to generate libraries of hypothetical structures for virtual screenings. For example, one can install functional groups on the linkers of a parent MOF, mimicking postsynthetic modification. Other applications of PoreMatMod.jl to modify crystal structure models include introducing defects with precision and correcting artifacts of X-ray structure determination (adding missing hydrogen atoms, resolving disorder, and removing guest molecules). The find-and-replace operations implemented by PoreMatMod.jl can be applied broadly to diverse atomistic systems for various in silico structural modification tasks.

Non-injective gas sensor arrays: identifying undetectable composition changes.

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH4/N2/CO2/C2H6mixtures relevant to natural gas sensing.

Synthesis of tetraphosphine macrocycles using copper(i) templates.

The synthesis of phosphine macrocycles is a relatively underdeveloped area and no standard synthetic routes have emerged. Accordingly, two general synthetic routes to tetradentate phosphine macrocycles were investigated. Both routes use Cu(i) ions as template ions because, unlike other metals such as Pd(ii) and Pt(ii), the Cu(i) ions can be removed from the macrocyclic complex without degrading the macrocycle ligand. The first route involves the coupling of two bidentate secondary phosphines bonded to Cu(i) using 1,3-dibromopropane or 1,4-dibromobutane. Using this route, tetradentate phosphine macrocycles with either -(CH2)3OCH3 or Ph groups bonded to the P atoms were synthesized. Macrocycle phosphines containing the -(CH2)3OCH3 groups were investigated for their potential water-solubility, but experiments showed these phosphines were not water soluble. The second synthetic route involved the alkylation of an open-chain, mixed tertiary-secondary, tetradentate phosphine coordinated to Cu(i). Following formation of the macrocyclic ligand, the Cu(i) template was removed by reaction with aqueous KCN to yield the free macrocyclic phosphine. This route was demonstrated for the preparation of the macrocyclic phosphine ligand 1,5,9,13-tetraphenyl-1,5,9,13-tetraphosphacycloheptadecane. Following demetallation, this macrocyclic ligand was coordinated to Fe(ii) and Co(ii) to form the corresponding macrocyclic phosphine complexes.