Maykel Cruz Monteagudo

Abstract 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.

Abstract The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.

Abstract Adenosine receptors (ARs) are signaling molecules ubiquitously expressed in a wide variety of tissues in the human body. ARs mediate physiological functions by interacting with four subtypes of G-protein-coupled receptors, namely A(1), A(2A), A(2B) and A(3). The A(3) AR, probably the most studied subtype, is also ubiquitously expressed, with high levels in peripheral organs and low levels in the brain. This type of AR is involved in a variety of important pathophysiological processes, ranging from modulation of cerebral and cardiac ischemic damage to regulation of immunosuppression and inflammation. Consequently, the development of potent and selective A(3) AR ligands as promising therapeutic options for a variety of diseases has been a prime subject of medicinal chemistry research for more than two decades. Among the plethora of approaches applied quantitative structure activity relationships (QSAR) stands out for being largely employed due to their potential to increase the efficiency at initial stages of the drug discovery process. So, we provide a review of the main QSAR studies devoted to the design, discovery and development of agonist and antagonist A(3) adenosine receptor ligands. Common pitfalls of these QSAR applications and the current trends in this area are also analyzed.

Abstract Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides own unique action mechanisms making peptide antibiotics an attractive therapeutic option against resistant bacteria. However, their high haemolytic activity lacks the selectivity required for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess greater selectivity for bacterial cells over erythrocytes. In this article, we introduce a chemoinformatics approach to simultaneously deal with these two conflicting properties consisting on a multi-criteria virtual screening strategy based on the use of a desirability-based multi-criteria classifier combined with similarity and chemometrics concepts. Here we propose a new quantitative feature encoding information related to the desirability, the degree of credibility ascribed to this desirability and the similarity of a candidate to a highly desirable query, which can be used as ranking criterion in a virtual screening campaign, the Desirability-Credibility-Similarity (DCS) Score. The enrichment ability of a multi-criteria virtual screening strategy based on the use of the DCS Score it is also assessed and compared to other virtual screening options. The results obtained evidenced that the use of the DCS score seems to be an efficient virtual screening strategy rendering promising overall and initial enrichment performance. Specifically, by using the DCS score it was possible to rank a selective antibacterial peptidomimetic earlier than a biologically inactive or non selective antibacterial peptidomimetic with a probability of ca. 0.9.

Abstract The adjustment of multiple criteria in hit-to-lead identification and lead optimization is a major advance in drug discovery. Thus, the development of approaches able to handle additional criteria for the early simultaneous treatment of the most important properties determining the pharmaceutical profile of a drug candidate is an emergent issue in this area. In this paper, we review a desirability-based multi-objective QSAR method allowing the joint handling of multiple properties of interest in drug discovery: the MOOP-DESIRE methodology. This methodology adapts desirability theory concepts allowing the holistic modeling of the many and conflicting biological properties determining the therapeutic utility of a drug candidate. Here we survey their suitability for key tasks involving the use of chemoinformatics methods in medicinal chemistry and drug discovery.