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.

Computational discovery of drug candidates.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.

ADMET prediction

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

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.

Chemical space docking

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.

Combinatorial library

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.

Computer aided drug design

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.

De novo Design

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 discovery and screening

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.

Fragment-based drug discovery (FBDD)

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.

High-throughput virtual screening

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.

Hit identification

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.