Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2022-04-29

Autores

M Natalia D S Cordeiro (Autor) FCUP; Amit K. Halder (Autor) FCUP; Ana S. Moura (Autor) FCUP

Revista

Título: International Journal Of Molecular Sciences

Vol. 23

Página Final: 4937

ISSN: 1661-6596

Editora: MDPI

Indexação

ISI Web of Knowledge - 1 Citação

Scopus

Outras Informações

ID Authenticus: P-00W-FQ7

DOI: 10.3390/ijms23094937

Abstract (EN): Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 21