Background: The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. Results: The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. Conclusions: In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

Background: A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors. Results: In this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance. Conclusions: The level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.

Fluorine is a common substituent in medicinal chemistry and is found in up to 50% of the most profitable drugs. In this study, a statistical analysis of the nature, geometry, and frequency of hydrogen bonds (HBs) formed between the aromatic and aliphatic C–F groups of small molecules and biological targets found in the Protein Data Bank (PDB) repository was presented. Interaction energies were calculated for those complexes using three different approaches. The obtained results indicated that the interaction energy of F-containing HBs is determined by the donor–acceptor distance and not by the angles. Moreover, no significant relationship between the energies of HBs with fluorine and the donor type was found, implying that fluorine is a weak HB acceptor for all types of HB donors. However, the statistical analysis of the PDB repository revealed that the most populated geometric parameters of HBs did not match the calculated energetic optima. In a nutshell, HBs containing fluorine are forced to form due to the stronger ligand–receptor neighboring interactions, which make fluorine the “donor’s last resort”.