Ákos Papp

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For general screening libraries, structural diversity descriptors and drug-likeness indicators still do not guarantee the in vivo bioavailability for the candidates, which is considered a major bottleneck in drug development. Early prediction of pharmacokinetics (log P, log D), metabolism, and toxicity makes it possible to deal with ADME (adsorption,(More)
Elimination of cytotoxic compounds in the early phases of drug discovery can save substantial amounts of research and development costs. An artificial neural network based approach using atomic fragmental descriptors has been developed to categorize compounds according to their in vitro human cytotoxicity. Fragmental descriptors were obtained from the(More)
A novel diversity assessment method, the Explicit Diversity Index (EDI), is introduced for druglike molecules. EDI combines structural and synthesis-related dissimilarity values and expresses them as a single number. As an easily interpretable measure, it facilitates the decision making in the design of combinatorial libraries, and it might assist in the(More)
In our days, pharmaceutical companies are screening millions of structures in silico. These processes require fast and accurate predictive QSAR models. Unfortunately, at the moment these models do not include information-rich quantum-chemical descriptors, because of their time-consuming calculation procedure. These challenges make indispensable the usage of(More)
The authors describe an innovative approach for designing novel inhibitors. This approach effectively integrates the emerging chemogenomics concept of target-family-based drug discovery with bioanalogous design strategies, including privileged structures, molecular frameworks as well as bioisosteric and bioanalogous/isofunctional modifications. The authors(More)
Modern approaches to chemistry and pharmacology deal with large-scale molecular design problems. The molecular design is essentially based on data warehousing and data mining. Data warehousing techniques are needed to collect relevant data from distributed and heterogeneous databases. Data mining techniques are used to build predictive quantitative(More)
An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the(More)
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