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Lead Finder is a computational chemistry tool designed for modelling protein-ligand interactions. It is used for conducting molecular docking studies and quantitatively assessing ligand binding and biological activity. It offers free access to users in commercial, academic, or other settings.
The original docking algorithm integrated into Lead Finder can be tailored for either quick but less accurate virtual screening applications or slower but more in-depth analyses. [1]
Lead Finder is used by computational and medicinal chemists for drug discovery, as well as pharmacologists and toxicologists involved in silico assessment of ADME-Tox properties. Additionally, it is used by biochemists and enzymologists working on modeling protein-ligand interactions, enzyme specificity, and rational enzyme design. Lead Finder's specialization in ligand docking and binding energy estimation is a result of its advanced docking algorithm and the precision with which it represents protein-ligand interactions. [2]
From a mathematical perspective, ligand docking involves the modelling of a multidimensional surface that describes the free energy associated with protein-ligand binding. This surface can be highly complex; with ligands possessing as many as 15-20 degrees of freedom, such as freely rotatable bonds.
Lead Finder's approach combines the use of genetic algorithm search, local optimization techniques, and knowledge gathered during the search process.
The Lead Finder scoring function represents protein-ligand interactions more precisely. The scoring function's model considers various types of molecular interactions.
In this scoring function, individual energy contributions are carefully adjusted with empirically derived coefficients tailored to objectives. Such as the prediction of binding energies, the ranking of energy for docked ligand poses, and the ordering of active and inactive compounds during virtual screening experiments. To achieve these goals, Lead Finder employs three types of scoring functions, based on the same set of energy contributions but with different sets of energy-scaling coefficients. [3]
Docking success rate was benchmarked as a percentage of correctly docked ligands for a set of protein-ligand complexes extracted from PDB. Results showed root mean squared deviations of 2 Å or less for 80-96% of the structures in the respective test sets (FlexX, [4] Glide SP, [5] Glide XP, [6] Gold, [7] [8] [9] LigandFit, [10] MolDock, [11] Surflex [12] ).
Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.
Molecular mechanics uses classical mechanics to model molecular systems. The Born–Oppenheimer approximation is assumed valid and the potential energy of all systems is calculated as a function of the nuclear coordinates using force fields. Molecular mechanics can be used to study molecule systems ranging in size and complexity from small to large biological systems or material assemblies with many thousands to millions of atoms.
Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.
In biochemistry and pharmacology, a ligand is a substance that forms a complex with a biomolecule to serve a biological purpose. The etymology stems from Latin ligare, which means 'to bind'. In protein-ligand binding, the ligand is usually a molecule which produces a signal by binding to a site on a target protein. The binding typically results in a change of conformational isomerism (conformation) of the target protein. In DNA-ligand binding studies, the ligand can be a small molecule, ion, or protein which binds to the DNA double helix. The relationship between ligand and binding partner is a function of charge, hydrophobicity, and molecular structure.
In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.
Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Protein–protein complexes are the most commonly attempted targets of such modelling, followed by protein–nucleic acid complexes.
In the context of chemistry, molecular physics, physical chemistry, and molecular modelling, a force field is a computational model that is used to describe the forces between atoms within molecules or between molecules as well as in crystals. Force fields are a variety of interatomic potentials. More precisely, the force field refers to the functional form and parameter sets used to calculate the potential energy of a system on the atomistic level. Force fields are usually used in molecular dynamics or Monte Carlo simulations. The parameters for a chosen energy function may be derived from classical laboratory experiment data, calculations in quantum mechanics, or both. Force fields utilize the same concept as force fields in classical physics, with the main difference being that the force field parameters in chemistry describe the energy landscape on the atomistic level. From a force field, the acting forces on every particle are derived as a gradient of the potential energy with respect to the particle coordinates.
Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates. There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands of various structures will bind to the protein, the ability for drug development to progress at a much faster rate becomes possible.
Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.
In molecular modelling, docking is a method which predicts the preferred orientation of one molecule to another when bound together in a stable complex. In the case of protein docking, the search space consists of all possible orientations of the protein with respect to the ligand. Flexible docking in addition considers all possible conformations of the protein paired with all possible conformations of the ligand.
In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.
Molecular binding is an attractive interaction between two molecules that results in a stable association in which the molecules are in close proximity to each other. It is formed when atoms or molecules bind together by sharing of electrons. It often, but not always, involves some chemical bonding.
Inte:Ligand was founded in Maria Enzersdorf, Lower Austria (Niederösterreich) in 2003. They established the company headquarters on Mariahilferstrasse in Vienna, Austria that same year.
LigandScout is computer software that allows creating three-dimensional (3D) pharmacophore models from structural data of macromolecule–ligand complexes, or from training and test sets of organic molecules. It incorporates a complete definition of 3D chemical features that describe the interaction of a bound small organic molecule (ligand) and the surrounding binding site of the macromolecule. These pharmacophores can be overlaid and superimposed using a pattern-matching based alignment algorithm that is solely based on pharmacophoric feature points instead of chemical structure. From such an overlay, shared features can be interpolated to create a so-called shared-feature pharmacophore that shares all common interactions of several binding sites/ligands or extended to create a so-called merged-feature pharmacophore. The software has been successfully used to predict new lead structures in drug design, e.g., predicting biological activity of novel human immunodeficiency virus (HIV) reverse transcriptase inhibitors.
Fragment-based lead discovery (FBLD) also known as fragment-based drug discovery (FBDD) is a method used for finding lead compounds as part of the drug discovery process. Fragments are small organic molecules which are small in size and low in molecular weight. It is based on identifying small chemical fragments, which may bind only weakly to the biological target, and then growing them or combining them to produce a lead with a higher affinity. FBLD can be compared with high-throughput screening (HTS). In HTS, libraries with up to millions of compounds, with molecular weights of around 500 Da, are screened, and nanomolar binding affinities are sought. In contrast, in the early phase of FBLD, libraries with a few thousand compounds with molecular weights of around 200 Da may be screened, and millimolar affinities can be considered useful. FBLD is a technique being used in research for discovering novel potent inhibitors. This methodology could help to design multitarget drugs for multiple diseases. The multitarget inhibitor approach is based on designing an inhibitor for the multiple targets. This type of drug design opens up new polypharmacological avenues for discovering innovative and effective therapies. Neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s, among others, also show rather complex etiopathologies. Multitarget inhibitors are more appropriate for addressing the complexity of AD and may provide new drugs for controlling the multifactorial nature of AD, stopping its progression.
AutoDock is a molecular modeling simulation software. It is especially effective for protein-ligand docking. AutoDock 4 is available under the GNU General Public License. AutoDock is one of the most cited docking software applications in the research community. It is used by the FightAIDS@Home and OpenPandemics - COVID-19 projects run at World Community Grid, to search for antivirals against HIV/AIDS and COVID-19. In February 2007, a search of the ISI Citation Index showed more than 1,100 publications had been cited using the primary AutoDock method papers. As of 2009, this number surpassed 1,200.
Chemoproteomics entails a broad array of techniques used to identify and interrogate protein-small molecule interactions. Chemoproteomics complements phenotypic drug discovery, a paradigm that aims to discover lead compounds on the basis of alleviating a disease phenotype, as opposed to target-based drug discovery, in which lead compounds are designed to interact with predetermined disease-driving biological targets. As phenotypic drug discovery assays do not provide confirmation of a compound's mechanism of action, chemoproteomics provides valuable follow-up strategies to narrow down potential targets and eventually validate a molecule's mechanism of action. Chemoproteomics also attempts to address the inherent challenge of drug promiscuity in small molecule drug discovery by analyzing protein-small molecule interactions on a proteome-wide scale. A major goal of chemoproteomics is to characterize the interactome of drug candidates to gain insight into mechanisms of off-target toxicity and polypharmacology.
Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design, fragment-based design, ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.
LeDock is a molecular docking software, designed for protein-ligand interactions, that is compatible with Linux, macOS, and Windows.
Shaomeng Wang is a Chinese-American chemist currently the Warner-Lambert/Parke-Davis Professor in Medicine at University of Michigan and a former Co-Editor-in-Chief at American Chemical Society's Journal of Medicinal Chemistry. A cited expert in his field, his interests are synthesis and design of moleculars, neurological diseases and computational and informatics. He was Elected as Fellow at the National Academy of Inventors in 2014. Dr. Wang was named to the AAAS Fellows Section on Pharmaceutical Sciences in 2019, and is the recipient of the Division of Medicinal Chemistry Award 2020 American Chemical Society.
