VAMMPIRE: A Matched Molecular Pairs Database for Structure-Based Drug Design and Optimization

Abstract

Structure-based optimization to improve the affinity of a lead compound is an established approach in drug discovery. Knowledge-based databases holding molecular replacements can be supportive in the optimization process. We introduce a strategy to relate the substitution effect within matched molecular pairs (MMPs) to the atom environment within the cocrystallized protein-ligand complex. Virtually Aligned Matched Molecular Pairs Including Receptor Environment (VAMMPIRE) database and the supplementary web interface ( http://vammpire.pharmchem.uni-frankfurt.de ) provide valuable information for structure-based lead optimization.

Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity

“…Mills et al 22 describe the identification of “well-matched SARs” as a purely empirical approach that could work because the binding pockets are similar or because of similar steric and conformational constraints. Weber et al 14 in their 3D method assume that potency changes relating to a particular matched pair transformation must depend on the protein atoms around that location and are able to rationalize a published SAR relationship for COX-2 on the basis of matched pairs from similar environments in factor Xa and thrombin.…”

Boosting Docking-Based Virtual Screening with Deep Learning

“…The idea of using information from closest neighbor atoms of both compound and protein have been successfully explored in previous work on structure-based drug design. 25 The basic features extracted from the context of an atom include the atom types, atomic partial charges, amino acid types and the distances from neighbors to the reference atom. For instance ( Figure 2), for the nitrogen atom (N 3 ) from THM compound, the vicinity with k c = 3 and k p = 2 is formed by N 3 , H and C from the compound, and the two atoms OE and CD from the residue Gln 125 in the protein Thymidine kinase (ID_PDB: 1kim).…”

“…19: end for 20: // column-wise max pooling 21: r = max(U, axis = 1) 22: // hidden and output layers 23: score = W 3 (W 2 r + b 2 ) + b 3 24: // returns normalized score 25: return e score[1] e score[0] + e score [1] into real-valued vectors (aka embeddings) by a lookup table operation. These embeddings contain features that are automatically learned by the network.…”

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

“…Weber et al [10] published in 2013 a strategy to relate the substitution effect within matched molecular pairs to the atom environment within the cocrystallized protein–ligand complex with the aim to predict ligand binding from extrapolation of the effect of the substitution with the molecular environment taken into account [49].…”

“…Several different applications for MMP analysis originating from industry or academia have been developed and published, highlighting its importance. Among others these include: Drug-Guru [6], [7], Buy me Grease [7], WizePairZ [8], T-Analyse and T-Morph [9], VAMMPIRE [10] as well as the Hussain-Rea MMP algorithm [11] (Table 1). The MMP concept has been further developed into Matched Pair Series [12], [13] or Matched Molecular Series (MMS) [14] to describe a set of compounds (not only a pair) differing by only a single chemical transformation.…”