We have analyzed hydrogen bonding in a number of species, containing from two to four hydrogen bonds. The examples were chosen in such a way that they would enable us to examine three different hydrogen bonds involving OH-O, NH-O, and NH-N. A common feature of the investigated systems is that they all are expected to exhibit resonance assisted hydrogen bonding (RAHB) in the electronic pi-framework. Our analysis was based on a recently developed method that combines the extended transition state scheme with the theory of natural orbitals for chemical valence (ETS-NOCV). We find that hydrogen bonding is associated with charge rearrangement in both the electronic sigma-framework (Deltarho(sigma)) and the electronic pi-framework (Deltarho(pi)). However the stabilization due to Deltarho(sigma) is four times as important as the stabilization (RAHB) due to Deltarho(pi). Stabilization due to the electrostatic interaction (DeltaE(elstat)) between the two monomers that are brought together to form the hydrogen bonds is also important. However DeltaE(el) cannot alone account for the strength of the hydrogen bonds as it is more than compensated for by the repulsive Pauli repulsion (DeltaE(Pauli)). When N' is part of an aromatic ring, N'H-O and N'H-N bonds are similar in strength to OH-O links involving carboxylic groups. However, NH-O bonds involving amide groups (-NH(2)) are considerably weaker than the OH-O links mentioned above. In systems with different hydrogen bonds, their relative strength is determined collectively in such a way as to optimize the total interaction. This can result in that one of the bonds (OH-O, NH-O, and NH-N) becomes particularly strong or exceptionally weak. Even within the same dimer two X'-HX bonds of the same type can show quite different strength.

Although the salt bridge is the strongest among all known noncovalent molecular interactions, no comprehensive studies have been conducted to date to examine its role and significance in drug design. Thus, a systematic study of the salt bridge in biological systems is reported herein, with a broad analysis of publicly available data from Protein Data Bank, DrugBank, ChEMBL, and GPCRdb. The results revealed the distance and angular preferences as well as privileged molecular motifs of salt bridges in ligand–receptor complexes, which could be used to design the strongest interactions. Moreover, using quantum chemical calculations at the MP2 level, the energetic, directionality, and spatial variabilities of salt bridges were investigated using simple model systems mimicking salt bridges in a biological environment. Additionally, natural orbitals for chemical valence (NOCV) combined with the extended-transition-state (ETS) bond-energy decomposition method (ETS–NOCV) were analyzed and indicated a strong covalent contribution to the salt bridge interaction. The present results could be useful for implementation in rational drug design protocols.

BackgroundThe paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods.ResultsThe 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.ConclusionsIn 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.