A series of renewable, long-chain, fatty acid-derived polyamides (PA) ranging from PA 6,14 to PA 6,18 were synthesized via polycondensation, yielding very high molecular weights and a remarkable property profile distinct from short-chain commercial grades. Most notably, synthesized polyamides exhibited good impact resistance, excellent stiffness-to-toughness balance and very low water absorption yet high oxygen and water vapour permeability; with this property profile being exemplified by PA 6,18. The increased repeating unit length and reduced number of amide linkages able to participate in interchain hydrogen bonding imparted a strong influence on material properties. The data highlights the benefits and technical advantages of utilising long-chain polyamides, while also significantly expanding the repertoire, knowledge and property profile of the long-chain aliphatic polyamide family, and providing a basis for further development of polyamides from renewable sources.

To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning modelswhether expert handcrafted features or learned molecular representations via graph-based neural network modelsyield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. To address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.

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BackgroundAccurate gene regulatory networks can be used to explain the emergence of different phenotypes, disease mechanisms, and other biological functions. Many methods have been proposed to infer networks from gene expression data but have been hampered by problems such as low sample size, inaccurate constraints, and incomplete characterizations of regulatory dynamics. Since expression regulation is dynamic, time-course data can be used to infer causality, but these datasets tend to be short or sparsely sampled. In addition, temporal methods typically assume that the expression of a gene at a time point depends on the expression of other genes at only the immediately preceding time point, while other methods include additional time points without any constraints to account for their temporal distance. These limitations can contribute to inaccurate networks with many missing and anomalous links.ResultsWe adapted the time-lagged Ordered Lasso, a regularized regression method with temporal monotonicity constraints, for de novo reconstruction. We also developed a semi-supervised method that embeds prior network information into the Ordered Lasso to discover novel regulatory dependencies in existing pathways. R code is available at https://github.com/pn51/laggedOrderedLassoNetwork.ConclusionsWe evaluated these approaches on simulated data for a repressilator, time-course data from past DREAM challenges, and a HeLa cell cycle dataset to show that they can produce accurate networks subject to the dynamics and assumptions of the time-lagged Ordered Lasso regression.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2558-7) contains supplementary material, which is available to authorized users.