Informatica

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2L-GO-Pharm using about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.

Over the past decades, many methods have been proposed to solve the linear or nonlinear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few main approaches to performing hyperspectral unmixing have emerged, such as nonnegative matrix factorization (NMF), linear mixture modelling (LMM), and, most recently, autoencoder networks. These methods use different approaches in finding the endmember and abundance of information from hyperspectral images. However, due to the huge variation of hyperspectral data being used, it is difficult to determine which methods perform sufficiently on which datasets and if they can generalize on any input data to solve hyperspectral unmixing problems. By trying to mitigate this problem, we propose a hyperspectral unmixing algorithm testing methodology and create a standard benchmark to test already available and newly created algorithms. A few different experiments were created, and a variety of hyperspectral datasets in this benchmark were used to compare openly available algorithms and to determine the best-performing ones.

Walras theory is well known and widely used in models of market economy. Various iterative methods are developed to search for the equilibrium conditions.