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Development of selective RAC1/KLRN inhibitors

Kubrat Neczaj-Hruzewicz et al. | Nov 04, 2024

Development of selective RAC1/KLRN inhibitors

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

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Strain-specific and photochemically-activated antimicrobial activity of berberine and two analogs

Sun et al. | Nov 17, 2020

Strain-specific and photochemically-activated antimicrobial activity of berberine and two analogs

In this study, the authors investigate the antimicrobial effects of berberine and berberine analogs. Berberine is extracted from plants and is a naturally occurring alkaloid, and is also excited photochemically. Using three different assays, the authors tested whether these compounds would inhibit bacterial growth. They found that these compounds were antibacterial and even more so when used with photoirradiation. This study has important antibacterial implications.

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Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Sikdar et al. | Jan 10, 2023

Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

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Utilizing sorbitol to improve properties of cellulose-based biodegradable hydrogels

Adler et al. | Jan 06, 2025

Utilizing sorbitol to improve properties of cellulose-based biodegradable hydrogels

Hydrogels are commonly used in medicine, pharmaceuticals, and agriculture. Hydrogels absorb water by swelling and re-release this water by diffusion. This study sought to synthesize a biodegradable, cellulose-based hydrogel that is more effective at absorbing and re-releasing water than those produced by current methods. We tested the compressive strength of both the dry and swollen gels and the tensile strength of the swollen gels to elucidate the gel structure.

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Modeling Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from Helium to Xenon using STO-nG basis sets

Gangal et al. | Oct 05, 2023

Modeling Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from Helium to Xenon using STO-nG basis sets

The energy of an atom is extremely useful in nuclear physics and reaction mechanism pathway determination but is challenging to compute. This work aimed to synthesize regression models for Pople Gaussian expansions of Slater-type Orbitals (STO-nG) atomic energy vs. atomic number scatter plots to allow for easy approximation of atomic energies without using computational chemistry methods. The data indicated that of the regressions, sinusoidal regressions most aptly modeled the scatter plots.

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