Development of Inorganic Molecular Shape Model Using 3D Printer to Strengthen Student's Conceptual Understanding

This research aims to develop a product in the form of an inorganic molecular shape model using a 3D printer, analyze the validity, practicality, effectiveness of the product, and strengthen students' conceptual understanding using the product. The method is Research and Development (R&D) based on the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. The supporting instruments for this research are product and instrument validation sheets, product readability questionnaires, pretest-posttest questions, question during the trial, observation sheets for students' conceptual understanding, and student response questionnaire to the product. The results showed that the product developed was following the ADDIE model because the five stages were carried out systematically; the product being developed has very high validity with an average percentage of 96.88%; the average percentage of practicality is 95.17% which is categorized as very practical, and the average percentage of effectiveness is 94.69% which is categorized as very effective; the product developed can strengthen students' conceptual understanding shown on the average percentage of conceptual understanding indicators achievement during the trial was 90.61% and the observation result was 94.69%, both of which belong to very high category. There was an increase in the average pretest value of 33.93 and post-test of 63.99. The results of the Rasch analysis after using this product showed that the consistency of students' answers was 0.71 (quite good), the quality of the items was 0.93 (very good), and the interaction between the consistency of students' answers was 0.68 (quite good). The product can be used to strengthen students' conceptual understanding on the topic of molecular shape.

Molecular Recognition and Shape Studies of 3- and 4-Substituted Diarylamide Quasiracemates

Families of quasiracemic materials constructed from 3- and 4-substituted chiral diarylamide molecular frameworks were prepared, where the imposed functional group differences systematically varied from H to CF3–9 unique components for each isomeric framework. Cocrystallization from the melt via hot stage thermomicroscopy using all possible racemic and quasiracemic combinations probed the structural boundaries of quasiracemate formation. The crystal structures and lattice energies (differential scanning calorimetry and lattice energy calculations) for many of these systems showed that quasienantiomeric components organize with near inversion symmetry and lattice energetics closely resembling those found in the racemic counterparts. This study also compared the shape space of pairs of quasienantiomers using an in silico alignment-based method to approximate the differences in molecular shape and provide a diagnostic tool for quasiracemate prediction. Comparing these results to our recent report on related 2-substituted diarylamide quasiracemates shows that functional group position can have a marked effect on quasiracemic behavior and provide critical insight to a more complete shape space, essential for defining molecular recognition processes.

Drug design, discovery and development and their safety or efficacy on human body

Drug Design, often mentioned as rational drug design or just rational design. It is defined as the study of the shape of molecules in order to determine how they will bind receptors on cells or combine with other molecules. It is based on molecular shape or architecture is an alternative to blindly testing hundreds of molecules to see if one or more of them will bind cellular or molecular targets. The drug is an organic molecule, when it is bind to target site it can either inhibit or activate the function of a bio-molecule which results in therapeutic benefit.

On the value of using 3D-shape and electrostatic similarities in deep generative methods

Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar 3D-shape and electrostatics. By performing comparisons of 3D-fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The comparison of electrostatic potential and molecular shape is performed using the new ESP-Sim python package, allowing the calculation of state-of-the-art partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. The new improved method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. All code is open-source and freely available.

Examining the Effects of Netropsin on the Curvature of DNA A-Tracts Using Electrophoresis

A-tracts are sequences of repeated adenine bases that, under the proper conditions, are capable of mediating DNA curvature. A-tracts occur naturally in the regulatory regions of many organisms, yet their biological functions are not fully understood. Orienting multiple A-tracts together constructively or destructively in a phase has the potential to create different shapes in the DNA helix axis. One means of detecting these molecular shape differences is from altered DNA mobilities measured using electrophoresis. The small molecule netropsin binds the minor groove of DNA, particularly at AT-rich sequences including A-tracts. Here, we systematically test the hypothesis that netropsin binding eliminates the curvature of A-tracts by measuring the electrophoretic mobilities of seven 98-base pair DNA samples containing different numbers and arrangements of centrally located A-tracts under varying conditions with netropsin. We find that netropsin binding eliminates the mobility difference between the DNA fragments with different A-tract arrangements in a concentration-dependent manner. This work provides evidence for the straightening of A-tracts upon netropsin binding and illustrates an artificial approach to re-sculpt DNA shape.

Exploring graph traversal algorithms in graph-based molecular generation

Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical

Spatial Information in the Emergence of Life

Information in living systems is part of a complex relationship between the internal organization and functionality of life. In a cell, both genetic-coding sequences and molecular-shape recognition are sources of biological information. For folded polymers, its spatial arrangement contains general references about conditions that shaped them, as imprints, defining the data for spatial (conformational) information. Considering the origin of life problem, prebiotic dynamics of matching and transfer of molecular shapes may emerge as a flow of information in prebiotic assemblages. The property of carrying information in molecular conformations is only displayed at this system organization level. Accordingly, spatial information is a resource for active system responses toward milieu disturbances. Propagation of resilient conformations could be the substrate for structural maintenance through dynamical molecular scaffolding. The above is a basis for adaptive behavior in potentially biogenic systems. Starting from non-structured populations of carrying-information polymers, in the present contribution, we advance toward an entire theoretical framework considering the active role of these polymers to support the emergence of adaptive response in systems that manage conformational information flow. We discuss this scenario as a previous step for the arising of sequential information carried out by genetic polymers.

Azaacenes Based Electroactive Materials: Preparation, Structure, Electrochemistry, Spectroscopy and Applications—A Critical Review

This short critical review is devoted to the synthesis and functionalization of various types of azaacenes, organic semiconducting compounds which can be considered as promising materials for the fabrication of n-channel or ambipolar field effect transistors (FETs), components of active layers in light emitting diodes (LEDs), components of organic memory devices and others. Emphasis is put on the diversity of azaacenes preparation methods and the possibility of tuning their redox and spectroscopic properties by changing the C/N ratio, modifying the nitrogen atoms distribution mode, functionalization with electroaccepting or electrodonating groups and changing their molecular shape. Processability, structural features and degradation pathways of these compounds are also discussed. A unique feature of this review concerns the listed redox potentials of all discussed compounds which were normalized vs. Fc/Fc+. This required, in frequent cases, recalculation of the originally reported data in which these potentials were determined against different types of reference electrodes. The same applied to all reported electron affinities (EAs). EA values calculated using different methods were recalculated by applying the method of Sworakowski and co-workers (Org. Electron. 2016, 33, 300–310) to yield, for the first time, a set of normalized data, which could be directly compared.