Chemometrics, that based prediction on the probability of chemical distribution to different systems, is highly important for physicochemical, environmental, and life sciences. However, the amount of ...information is huge and difficult to analyze. A multi-system partition Complex Network (MSP-CN) may be very useful in this sense. We define MSP-CNs as large graphs composed by nodes (chemicals) interconnected by arcs if a pair of chemicals have similar partition in a given system. Experimental quantification of partition in many systems is expensive, so we can use a Quantitative Structure–Partition Relationship (QSPR) model. Unfortunately, with classic QSPR we need to use one model for each system. Here construct the first MSP-CN based on a multi-target QSPR (mt-QSPR). The model is based on the spectral moments (
π
k
) of a molecular Markov matrix weighted with atomic parameters that depend on both the nature of the atom and the partition system. The mt-QSPR predicts 90.6% of 413 compound/system pairs in training series and 90.0% in validation. The MSP-CN predicted presents 413 nodes, 2060 edges, average node degree 9.9, and only 7.7% drugs are unconnected. The model was used to study the biophysical phenomena of transport or distribution of G1 (a novel antimicrobial drug) to different rat tissues. Predicted probabilities (
P) coincide with low experimental partition coefficients (logPC) reported herein by the first time in skin (
P
=
0.455; logPC
=
−
0.02
<
0
→
U), heart (0.453; −
0.02
→
U), and brain (0.324; −
0.34
→
U). The Kamada–Kawai algorithm evidenced the community structure of the MSP-CN and clusters G1 into three different communities of the U-type drugs. These results coincide with the low distribution of G1 to these tissues and consequently have low expected drug side effect.
Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the ...phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(4′-chlorophenyl)-2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure−activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOP-DESIRE methodology was used for this purpose providing reliable ranking models that can be used later.
Background: Virtual methodologies have become essential components of the drug discovery
pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular
...targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the
Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies
for the treatment of Parkinson's disease.
Methods: In this paper we propose a structure-based methodology, which is extensively validated, for the
discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves
molecular docking studies against both receptors and the evaluation of different scoring functions fusion
strategies for maximizing the initial virtual screening enrichment of known dual ligands.
Results: The developed methodology provides high values of enrichment of known ligands, which
outperform that of the individual scoring functions. At the same time, the obtained ensemble can be
translated in a sequence of steps that should be followed to maximize the enrichment of dual target
Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors.
Conclusion: Information relative to docking scores to both targets have to be combined for achieving high dual
ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable
strategy for improving the enrichment relative to single scoring function in virtual screening experiments.
Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the ...limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made.
In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations.
Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening.
Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson's disease therapeutics.
Most of present mathematical models for biological activity consider just the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular ...descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this mathematical model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, a general Markov model is developed that describes 19 different drugs side effects grouped in eight affected biological systems for 178 drugs, being 270 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations, (18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations, (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena, (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations, (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations, (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation, (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations, (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations, (31 out of 41)/(23 out of 31). Additionally a back-projection analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physical interpretation of the models obtained. This article develops a mathematical model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction ((A)pi(k)(j)) by the first time.
MARCH-INSIDE methodology and a statistical classification method--linear discriminant analysis (LDA)--is proposed as an alternative method to the Draize eye irritation test. This methodology has been ...successfully applied to a set of 46 neutral organic chemicals, which have been defined as ocular irritant or nonirritant. The model allow to categorize correctly 37 out of 46 compounds, showing an accuracy of 80.46%. Specifically, this model demonstrates the existence of a good categorization average of 91.67 and 76.47% for irritant and nonirritant compounds, respectively. Validation of the model was carried out using two cross-validation tools: Leave-one-out (LOO) and leave-group-out (LGO), showing a global predictability of the model of 71.7 and 70%, respectively. The average of coincidence of the predictions between leave-one-out/leave-group-out studies and train set were 91.3% (42 out of 46 cases)/89.1% (41 out of 46 cases) proving the robustness of the model obtained. Ocular irritancy distribution diagram is carried out in order to determine the intervals of the property where the probability of finding an irritant compound is maximal relating to the choice of find a false nonirritant one. It seems that, until today, the present model may be the first predictive linear discriminant equation able to discriminate between eye irritant and nonirritant chemicals.
Desirability theory (DT) is a well-known multi-criteria decision-making approach. In this work, DT is employed as a prediction model (PM) interpretation tool to extract useful information on the ...desired trade-offs between binding and relative efficacy of N(6)-substituted-4'-thioadenosines A3 adenosine receptor (A3AR) agonists. At the same time, it was shown the usefulness of a parallel but independent approach providing a feedback on the reliability of the combination of properties predicted as a unique desirability value. The appliance of belief theory allowed the quantification of the reliability of the predicted desirability of a compound according to two inverse and independent but complementary prediction approaches. This information is proven to be useful as a ranking criterion in a ligand-based virtual screening study. The development of a linear PM of the A3AR agonists overall desirability allows finding significant clues based on simple molecular descriptors. The model suggests a relevant role of the type of substituent on the N(6) position of the adenine ring that in general contribute to reduce the flexibility and hydrophobicity of the lead compound. The mapping of the desirability function derived of the PM offers specific information such as the shape and optimal size of the N(6) substituent. The model herein developed allows a simultaneous analysis of both binding and relative efficacy profiles of A3AR agonists. The information retrieved guides the theoretical design and assembling of a combinatorial library suitable for filtering new N(6)-substituted-4'-thioadenosines A3AR agonist candidates with simultaneously improved binding and relative efficacy profiles. The utility of the desirability/belief-based proposed virtual screening strategy was deduced from our training set. Based on the overall results, it is possible to assert that the combined use of desirability and belief theories in computational medicinal chemistry research can aid the discovery of A3AR agonist candidates with favorable balance between binding and relative efficacy profiles.
A capacidade de melhorar o perfil farmacêutico de um fármaco baseado exclusivamente na sua eficácia terapcêutica ha sido freqüentemente superestimada. O ajuste de critérios múltiplos na identificação ...de candidatos potenciais (hit-to-lead identification) e na otimização dos líderes (lead optimization) é considerado um progresso fundamental no processo de descobrimento racional de fármacos. Assim, o desenvolvimento de aproximações capazes de manejar critérios adicionais para o tratamento prematuro e simultâneo das propriedades mais importantes que determinam o perfil farmacêutico de um candidato de fármaco como a sua potência, segurança, e biodisponibilidade, é uma questão emergente no processo de descobrimento e desenvolvimento de fármacos.Nesta Tese, é introduzido um método de otimização multi-objetivos (OMO) baseado nas funções de conveniência de Derringer, que permite conduzir estudos QSAR globais considerando simultaneamente a potência, segurança e/ou a biodisponibilidade de um conjunto de candidatos de fármaco. Os resultados do processo de OMO (os níveis das variáveis explicativas que simultaneamente produzem o melhor equilibrio possível entre as propriedades que determinam um ótimo candidato de fármaco) é usado para a implementação de um método de ordenação, também baseado na aplicação de funções de conveniência. Este método permite ordenar grandes bibliotecas de compostos (reais ou virtuais) com propriedades farmacêuticas desconhecidas de acordo com o grau de semelhança com o candidato ótimo previamente determinado.O processo inteiro é condensado em uma metodologia que nós decidimos nomear como MOOP-DESIRE, acrônimo em idioma inglês para MultiObjective OPtimization based on the Desirability Estimation of Several Interrelated REsponses. A sua conveniência para as principais tarefas que envolvem o uso de métodos quimioinformáticos no descobrimento de fármacos - desenho de fármacos, ordenação de bibliotecas, e screening virtual - é avaliado além do uso da Teoria da Conveniência como uma ferramenta para a interpretação de modelos de predição multi-critérios. Cada tarefa foi avaliada mediante quatro conjuntos de dados diferentes permitindo a verificação do desempenho da metodologia na tarefa correspondente, representando cada uma de estas um problema atual na área de descobrimento de fármacos. Os resultados globais obtidos sugerem que a identificação de hitscom um equilibrio apropriado entre potência e segurança, em lugar de hitscompletamente otimizados baseados unicamente na potência, pode facilitar a transição "hit-to-lead" e aumentar a probabilidade do candidato para evoluir num fármaco próspero. Assim, é possível afirmar que a metodologia de OMO proposta pode ser considerada uma valiosa ferramenta para o processo de descobrimento e desenvolvimento racional de fármacos.
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 A1, A2A, A2B and A3. The A3 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 A3 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 A3 adenosine receptor ligands. Common pitfalls of these QSAR applications and the current trends in this area are also analyzed.
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