Nowadays, computational methods are routinely used to accelerate drug discovery process. In drug design and discovery, multiple computational chemistry approaches are employed to calculate and predict events, such as the drug binding to its target and the chemical properties for designing potential new drugs. At Alfa Chemistry, we apply the knowledge of computational chemistry to create various physics-based algorithms, and use computers to simulate chemical events and calculate chemical properties of atoms and molecules.
Application of Computational Techniques in Each Phase of Drug Design
- The hit identification phase: computational methods are used to identify chemical compounds with a promising activity toward the target.
- The hit-to-lead phase: hit compounds are improved in potency against the target with the application of various computational models and algorithms.
- The lead optimization phase: lead compounds are optimized, generating drug-like molecules by employing computational tools.
Figure 1. Computational discovery of drug candidates. (Baishakhi, D.; et al. 2019)
Computational Drug Design Services
With the deepening of quantum chemistry research and the improvement of computing software, they play an important role in the process of scientific research and chemistry teaching. We apply multiple quantum chemistry computing software to design novel drug. To make it easier and better for researchers, Alfa Chemistry provides practical drug design services in a competitive fashion. We have prepared the most convenient services for you.
In order to improve structural data and optimize the statistical methods, scientists develop 2D-QSAR models during the process of optimization of a chemical series towards a candidate for clinical trials. Our well-designed 2D-QSAR methods are useful in the new molecules design and prediction of their bioactivity.
Our experts can build reliable models through 3D-QSAR. The three-dimensional contour diagrams of 5 fields (stereoscopic field, electrostatic field, hydrophobic field, hydrogen bond donor field and hydrogen bond acceptor field) can clearly explain the structure-activity relationship of the compound. We comprehensively analyze and study the structure that affects the activity of the compound feature. At Alfa Chemistry, several new drug molecules have been designed through the optimization and modification of the molecular structure.
One of the main reasons for the failure of drug development is efficacy and safety defects, which are largely related to absorption, distribution, metabolism and excretion (ADME) characteristics and various toxicities (T). Therefore, there is an urgent need for a rapid ADMET assessment to minimize failures in the drug discovery process. Alfa Chemistry facilitates the drug design process through rapid ADMET prediction of virtual screening and prioritization of chemical structures.
Atomistic and coarse-grained modeling of pharmaceutical formulations have been applied to study a diversity of complex systems. At Alfa Chemistry, our experienced experts have established a multiscale (dual resolution) modeling approach which combines an atomistic and coarse-grain (MARTINI) force field to perform accurate analysis of static atomic structures.
Scientists have applied chemical space docking in the virtual screening to deal with multiple molecules occurring in combinatorial chemical spaces. We have combined the application of machine learning to predict actives based on their binding mode after a docking procedure with subsequent scoring of the complexes.
A large number of molecules are able to be synthesized rapidly and at lower cost using combinatorial chemistry synthetic methods. Alfa Chemistry has applied common combinatorial library construction and centralized combinatorial library design methods to support molecular docking-based and pharmacophore-based virtual screening.
As one of the most important methods for design of lead compound, computer-aided drug design is widely applied to study drug-efficacy models of the effects of drugs on targets. Our advanced computer-aided drug design platform supports molecular docking simulation, molecular dynamics simulation, quantitative structure-activity relationships modeling and quantum mechanics.
Alfa Chemistry can provide high-quality de novo design services to deliver new chemical structures and the required molecular characteristics in the best way. We are capable of performing machine learning (ML), artificial intelligence (AI) and other computational methods to carry out atom-based, fragment-based and reaction-based de novo design.
Dominant skeleton is the core structure of small molecules. We can separate and compare the dominant skeleton of active compounds, and perform computational searches on molecules with similar activities, to improve the efficacy and drug-like properties of molecules.
As an important way to identify new lead compounds, fragment-based drug discovery has been widely applied in HTS screening. Alfa Chemistry has established a calculation-based FBDD workflow to screen out the hit fragment molecules with biological activity.
We can conduct either ligand-based or structure-based high-throughput virtual screening depending on the specific drug design project. Various technologies such as docking, simulation, kinetics, and ligand design methods are available to support the structure-based methods. Moreover, we use multiple descriptors of molecular features to predict activity depending on its similarity/dissimilarity to previously known active ligands in ligand-based high-throughput virtual screening process.
Scientists conduct hit identification to discover compounds with the desired activity against a fully validated target. At Alfa Chemistry, our teams have applied structure-based, ligand-based, pharmacophore-based virtual screening and shape-based, similarity-based and fragment-based drug design to complete the hit identification.
Figure 2. The application of in silico tools in the development of anti cancer drugs. (Baishakhi, D.; et al. 2019)
Homology modeling has been used to study the interaction between the target and drug, and analyze its 'structure-function' relationship. We have established a standardized homologous modeling process to predict the three-dimensional structure of a protein of interest through computer simulation and calculation.
Lead optimization is a process in which the chemical structure of a confirmed hit is altered. Aim of the lead optimization is to obtain a preclinical candidate by improving the hit compound. Our groups can provide a series of lead optimization approaches such as in vitro ADMET, in silico and PBPK modelling, metabolites identification and physicochemical analysis.
We apply ligand based drug design when the 3D information of receptor is absent or unavailable. Alfa Chemistry supports quantitative structure activity relationships (QSAR) analysis, pharmacophore modeling and similarity searches to study the binding mode of the compound and the target, assisting in the lead identification and optimization process.
Our teams are committed to identifying the optimal supramolecular interactions with a specific biological target structure using the ligand-based pharmacophore. In order to create a ligand-based pharmacophore, our scientists employ multiple strategies such as using superimposing active compounds to create a pharmacophore, pharmacophore feature extraction method, pharmacophore algorithms and software packages.
Aiming to provide mlecular dynamics simulation, a large number of advanced computer tools are used to simulate the movement of molecules and atomic systems. We apply binding free energy calculation with MM/PB(GB)SA, cluster analysis, nonpolar solvation energy calculation with MM/PB(GB)SA, polar solvation energy and entropy calculation with MM/PB(GB)SA, principal component analysis (PCA), targeted molecular dynamics (TMD) simulation and umbrella sampling simulation to complete the molecular dynamics simulation.
In molecular simulation, a diversity of physical and chemical properties of molecular systems can be obtained by simulating molecular structure and behavior with atomic-level molecular models. At Alfa Chemistry, both quantum mechanics simulation and classical mechanics simulation are available to perform molecular dynamic simulation in drug design.
Predicting property of drug-likeness molecules with high precision is a challenging task in drug design. We provide easy-to-use models and computational tools such as deep learning, ab initio prediction, graph neural network, artificial and machine learning techniques to offer property prediction of drug-like molecules services rapidly and effectively.
We are able to design a variety of new drugs with different specific functions based on the deep understanding of the structure and function of various receptors. Our platforms provide multiple solutions such as machine learning method, force field, conformation analysis services, quantum mechanics and QSAR analysis.
The insolubility of novel drug candidates has become a major problem of current drug development. Alfa Chemistry provides various computational tools to solve this issue by screening for suitable solvents or by identifying potential novel salt/co-crystal formers that increase bioavailability. Our strategies include conductor-like screening model, graph neural network (GNN), synthon matching and fabian's method.
In order to improve the stability of the drug, scientists replace the scaffold that is prone to metabolism with a metabolically stable, low-toxic skeleton. At Alfa Chemistry, we have created a novel technique in which the CAVEAT style functionality is combined with the MOE pharmacophore tool for 3D scaffold replacement.
Crystal structure can significantly affect the stability, bioavailability and efficacy of the drug. Our drug crystal structure prediction services are developed based on computational chemistry and artificial intelligence methods. Combined with our core technology of crystal structure search algorithm, force field, quantum dynamics calculation and crystal free energy calculation, high-precision prediction of the relative thermodynamic stability of different crystal structures is able to be achieved.
Alfa Chemistry has built an experienced computational science team to perform structure-based drug design techniques to carry out drug discovery projects. Our capabilities include virtual screening, flexible target sampling, skeleton transitions, de novo design, and estimation of the affinity between ligands and target proteins. We have abilities in molecular docking, crystal structure prediction, homology modeling, structure-based virtual screening, molecular dynamics simulation, quantum mechanics (QM) calculation and fragment-based drug design (FBDD).
Structure-based pharmacophore model has played an essential role in the discovery of new active molecules in virtual screening. Our professional teams have created multiple methods to construct pharmacophore models including structure-based 3D pharmacophore identification, pharmacophore modeling based on different targets, feature-based pharmacophore modeling and molecular field-based pharmacophore modeling.
Target analysis and pocket finding have been used for ligand binding studies. Our scientists have rich experience in finding and determining the active pocket and docking position of the target. Moreover, we can screen our more druggable and potential targets for our customers with our deep understanding in the current competitive landscape of targets.
- Flexible and advanced computational methods
- Personalized and customized innovative scientific research services
- Cost-efficient and time-saving
Our drug design services remarkably reduce the cost and promote further experiments. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
- Narouz, M. R.; et al. N-heterocyclic carbene-functionalized magic-number gold nanoclusters. Nature Chemistry. 2019.