A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents

<div><p>Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.</p></div

Summary of the performance of the developed ensemble models.

<p>Summary of the performance of the developed ensemble models.</p

Comparison between the accuracy of the best ensemble for each endpoint and the mean accuracy of the base models they are composed by.

<p>The mean accuracy of the base models is shown using squares-pattern bars.</p

Performance of the base models.

<p>Performance of the base models.</p

Accumulative curves for the 18 top-performing VS protocols.

<p>Results are presented for a) Whole screening. b) Top 5% of screened data.</p

Summary of the data sets curation workflow.

<p>Results are summarized for the: a) AGS, b) NCI-N87 and c) SNU-1 data sets.</p

VS performance of the multi-target protocols.

<p>VS performance of the multi-target protocols.</p

<i>Dry</i> selection and <i>wet</i> evaluation for the <i>rational</i> discovery of new anthelmintics

<p>Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a <i>TOMOCOMD-</i><i>CARDD</i> (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmintics. The model shows an accuracy of 86.84% in the training set and 94.44% in the external prediction set. Finally, we carry out an experiment to predict the biological profile of our ‘in-house’ collections of indole, indazole, quinoxaline and cinnoline derivatives (∼200 compounds). Subsequently, we selected a group of nine of the theoretically most active structures. Then, these chemicals were tested in an <i>in</i> <i>vitro</i> assay and one good candidate (VA5-5c) as fasciolicide compound (100% of reduction at concentrations of 50 and 10 mg/L) was discovered.</p