A graph-based approach to construct target-focused libraries for virtual screening

Abstract

BackgroundDue to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family. MethodsAssuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns.ResultsWe conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated.ConclusionseSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstractAssuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. Here, we developed eSynth, an automated method to synthesize new compounds by reconnecting these building blocks following the connectivity patterns via an exhaustive graph-based search algorithm. eSynth opens up a possibility to rapidly construct virtual screening libraries for targeted drug discovery

Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple)

“… 8 − 11 Classical de novo strategies can potentially populate new areas of chemical space, 12 − 16 and thus, programs have been developed to disconnect molecules following retrosynthesis rules 17 , 18 producing fragments that can be used later on to construct new libraries. 19 Nevertheless, significant challenges when reaching the synthesis stage might prevent those new molecular entities from being prepared and, ultimately, becoming useful chemical probes. 13 In addition, time pressure in drug-discovery campaigns demands new tools to improve the identification of hits and streamline their optimization into lead compounds.…”

Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag

“…Although this information could help explore pharmacologically relevant regions of the diverse chemical space, 9 many existing fragmentation tools, e.g. Fragmenter 19 and molBLOCKS, 20 do not consider the chemical context of the fragments.…”

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

“…Thus there is still absence of freely available, easy to use and unified platforms for generating molecular descriptors of DNAs/RNAs, proteins, small molecules and their interactions. A method to understand protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information was proposed by: Misagh Naderi [7] in a graph-based approach to construct Target focused libraries for virtual screening. In the paper of Deep Belief Networks for Ligand-Based Virtual Screening of Drug Design by Aries Fitriawan [8] suggest about the virtual screening method in drug discovery the author talks about finding a new method for ligand-based virtual screening using machine learning technique here the classification has been done by using Deep Belief Networks (DBN) method which permit any inter-layer model of Restricted Boltzmann Machine (RBM) to receive a different depiction of the data from its output.…”