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Abstract An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds. [ABSTRACT FROM AUTHOR]

Single target based approaches often proved to be unsuccessful in complex multigenic diseases such as cancer or schizophrenia. Multi-target drugs can be more efficacious in this regard by modulating multiple processes in the organism. According to the theory of polypharmacology, bioactive molecules possess characteristic interaction patterns that are responsible for their effects and side-effects and getting acquainted with this typical profile is increasingly desired to promote pharmaceutical research and development. There is a novel way of approaching polypharmacology that takes into account the interaction of molecules to a set of proteins that are not necessarily known biological targets of the compounds. Applying a carefully selected panel of proteins that can model the possible interactions a molecule can exert when administered to a human body, holds out a promise of biological activity prediction. This review aims to summarize a number of such bioactivity profiling-based approaches set up recently and present their application areas within the drug discovery field., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

The pharmacophore concept in modern drug research is highlighted and the most important use examples and success stories are reviewed. These include papers from method development as well as from application areas. As indicated by the number of publications available, the pharmacophore approach has proven to be extremely useful as interface between medicinal and computational chemistry, both in virtual screening and library design for efficient hit discovery, but also in the optimization of lead compounds to clinical candidates. Recent studies focus on the usage of parallel screening using pharmacophore models for bio-activity profiling and early stage risk assessment of potential side effects and toxicity due to interaction of drug candidates with anti-targets., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Far from the traditional view of selective drug-target interactions, the recent accumulation of large amounts of interaction data for small-molecule drugs and protein targets requires innovative visualisation and analysis tools that are able to deal with what has become a truly complex system. In this context, network theory offers both a robust and illustrative framework to investigate drug-target connections and has been swiftly and widely embraced by the chemical biology and molecular informatics communities. A survey of the most recent applications of drug-target networks to detect cross-pharmacology relationships among targets and to identify new targets for known drugs is provided. Finally, some of the current limitations are also discussed, including the actual completeness of interaction data and the information loss intrinsically associated with the one-mode projection of drug-target networks., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)