Chemoinformatics in Multi-target Drug Discovery for Anti-cancer Therapy: In Silico Design of Potent and Versatile Anti-brain Tumor Agents

Abstract

A brain tumor (BT) constitutes a neoplasm located in the brain or the central spinal canal. The number of new diagnosed cases with BT increases with the pass of the time. Understanding the biology of BT is essential for the development of novel therapeutic strategies, in order to prevent or deal with this disease. An active area for the search of new anti-BT therapies is the use of Chemoinformatics and/or Bioinformatics toward the design of new and potent anti-BT agents. The principal limitation of all these approaches is that they consider small series of structurally related compounds and/or the studies are realized for only one target like protein. The present work is an effort to overcome this problem. We introduce here the first Chemoinformatics multi-target approach for the in silico design and prediction of anti-BT agents against several cell lines. Here, a fragment-based QSAR model was developed. The model correctly classified 89.63% and 90.93% of active and inactive compounds respectively, in training series. The validation of the model was carried out by using prediction series which showed 88.00% of correct classification for active and 88.59% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BT activity were calculated. Several fragments were identified as potential substructural features responsible of anti-BT activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-BT agents.

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

“…For this reason, we decided to call this strategy as ALMA (Assessing Links with Moving Averages) models. Speck-Planche and Cordeiro reported different multi-target or multi-output models using the same type of ALMA models [ 44 , 45 , 46 ].…”

A Comparative Study of 2D UMI and 3D Zernike Shape Descriptor for ATS Drugs Identification

“…However, most soft computing techniques introduced recently are applied in the drug design and drug discovery domain [7,8]. Some researchers also explore the usage of principal component analysis (PCA) for screening drugs of abuse [9], neural network for structures to quantitative analysis [10,11], neural network and knearest neighbor for quantitative analysis of Raman spectroscopy data [12], and the most recent, PCA and neural network to classify molecular data [13].…”

SAR, QSAR and Docking of Anticancer Flavonoids and Variants: A Review

“…Topological and substructural descriptors have been recently employed in several anticancer QSAR studies using sets of small molecules of several classes with activities against brain tumor cells lines [24], anti colorectal cancer cells [25], breast cancer cell lines [26] and, human carcinoma of the nasopharynx [27]. Recently, some studies of Quantitative Structure-Disease Relationship (QSDR) used topological index to predict protein properties linked with colon cancer [28][29][30], human breast cancer [30], and antioxidant properties [31].…”

“…Future perspectives in the discovery of flavonoids with anticancer activity may be focused on the use of novel and recently reported QSAR studies, which allow simultaneous prediction and virtual screening of compounds with the desired biological activity [24][25][26][121][122][123][124][125][126][127][128][129][130][131][132][133][134][135][136]. These promising methodologies could be of great help in chemotaxonomic studies, the fast and efficient detection of flavonoids from different plant species, and at the same time, with the computer-aided selection of these versatile natural products as anticancer agents.…”