Multi-objective latent space optimization of generative molecular design models.

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization (LSO). In this paper, we propose a multi-objective LSO method that can significantly enhance the performance of generative molecular design (GMD).

Molecular Signature of miR-34a/NEAT-1/p53 Axis in Mycosis Fungoides.

Background: Mycosis fungoides (MF) is a type of cutaneous T-cell lymphoma where red rash exists on the skin. Understanding the role of miRNAs and ncRNAs in p53-response has become an open discussion, as they can regulate p53 or its downstream targets, and ncRNAs themselves.

Objectives: To evaluate the serum levels of NEAT-1, miR-34a, and p53 in MF patients and its relation to healthy controls to indicate whether it has a potential role in the pathogenesis of the disease.

Partial purification and properties of adenosine triphosphatase (ATPase) from liver fluke Fasciola hepatica.

Objective: The adenosine triphosphatase (ATP phosphohydrolase, EC 3.6.1.