Bioactive Extracts: Strategies to generate Diversified Natural Product Like Libraries

Natural products have stimulated chemists owing to their abundant structural diversity and complexity. Indeed, natural products have performed an essential role, particularly in the cure of cancerous and infectious diseases, thereby posing medicinal researchers with a scope of unexplored chemotypes for the innovation of new drugs. Fusion of chemical derivatization and combinatorial synthesis forms the basis of the concept of chemo diversification of plants. Diverse libraries of natural product analogs are constructed through existing biological and chemical approaches using unique schemes to expand natural product frameworks. This review aims to present several approaches employed to offer innovative opportunities to synthesize NP-inspired compound libraries. Reactive molecular fragments present in most natural products are chemically converted to chemically engineered extracts (CEEs) or semisynthetic compounds constituting distinct libraries. Bio-guided isolation for natural products required vital tools like reverse phase chromatography and bioautographic assays. Different established strategies from DTS, BIOS, CtD, FOS, FBDD to Late-stage diversification facilitate the expansion of molecules with physicochemical properties. In particular, fragment-like natural products with novel skeletons may be used as preliminary points for chemical biology and medicinal chemistry programs with great capacity. In this review, we sum up how NPs have proven fruitful for the novel methodologies responsible for the diversification of complex natural products; thereby, it is worthy of going over the upcoming integration of natural products with combinatorial chemistry.

Explainable Graph Neural Networks for Organic Cages

The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed “shape persistency” is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4+6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradient. With the added explicability of the GNNs, it is possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights to the discovery of future materials.

Fragment-based Sequential Translation for Molecular Optimization

Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instead propose a flexible editing paradigm that generates molecules using learned molecular fragments---meaningful substructures of molecules. To do so, we train a variational autoencoder (VAE) to encode molecular fragments in a coherent latent space, which we then utilize as a vocabulary for editing molecules to explore the complex chemical property space. Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties. Empirical evaluation shows that FaST significantly improves over state-of-the-art methods on benchmark single/multi-objective molecular optimization tasks.

Fragment-based Sequential Translation for Molecular Optimization

Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instead propose a flexible editing paradigm that generates molecules using learned molecular fragments---meaningful substructures of molecules. To do so, we train a variational autoencoder (VAE) to encode molecular fragments in a coherent latent space, which we then utilize as a vocabulary for editing molecules to explore the complex chemical property space. Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties. Empirical evaluation shows that FaST significantly improves over state-of-the-art methods on benchmark single/multi-objective molecular optimization tasks.

Nanocomposite Obtained in the Plasma of a Pulsed High Voltage Discharge Using Nickel Electrodes and PTFE

In the plasma of pulsed high-voltage discharge, initiated between nickel electrodes in air, when the fluoroplastic is placed in the discharge gap, powder nanocomposite material has been synthesized. The nanocomposite contains NiF2 nanoparticles less than 5 nm in size, dispersed in a matrix consisting of carbon and fluorocarbon substances. The carbonaceous substance contains nanoscale disordered graphite-like regions. The fluorocarbon component of the composite contains fragments of PTFE molecules and fluorocarbon molecular fragments that differ in structure from PTFE molecule’s structure. After annealing the composite in air at 773 K, the initial nanocomposite is transformed into a nanocomposite containing nanosized PTFE and nanoparticles of NiF2 less than 5 nm in size, scattered in a matrix composed of nanographite and low-layer nanosized graphene, after aneling at 1173 K into a material containing NiO nanoparticles less than 10 nm in size. After annealing of the initial nanocomposite in argon atmosphere at 1073 K, the obtained nanocomposite contains Ni nanoparticles with sizes less than 5 nm and carbon and fluorocarbon components. The magnetic susceptibility of the unannealed nanocomposite is investigated. A transition to the antiferromagnetic phase at 73 K was detected. At T = 4K, exchange bias interaction of the AFM / FM type takes place in the composite. There is divergence of the FC and ZFC curves, which can be explained by the presence of a superparamagnetic phase or a spin glass phase in the sample. The field dependences of the magnetic susceptibility measured at T = 300 K show sharp changes that occur at certain values of the magnetic field. Elucidation of the nature of these changes requires additional research.

Low Temperature Resistance Increase for Bitumen by Compounding with Tar

In this paper, a conventional road bitumen with penetration grade 100–130 is compounded with tar in order to obtain bitumen with improved low temperature resistance. The low temperature (at −24 °C, −30 °C and −36 °C) resistance of the virgin bitumen and the compounded one is evaluated by testing on a bending beam rheometer. It was found that the optimum compounding (20% of tar by weight) decreases the stiffness essentially (from 18% to 34%), i.e., it increases the low temperature resistance of the bitumen. The stiffness decreases in the compounded bitumen can be explained by quantitative variations in its group chemical composition and molecular fragments. Group chemical composition has been determined by the method of absorption chromatography, and the fragments of molecules are identified by NMR-spectroscopy.