Virtual Screening in Drug Discovery

Our structural biology department offers services supporting structure-based drug discovery from determination of novel targets to final structures. Our platform is one of the earliest established structural biology platforms in China and has been certified by the Shanghai Government.

– Computational Biology & Molecular Modeling

– Structural-Based Drug Design (SBDD)

– De novo Drug Design

– Virtual Drug Screening

– Quantitative Structure-Activity Relationship (QSAR)

– ADMET Property Optimization

Virtual Screening (VS) methods have emerged as an adaptive response to massive throughput synthesis and screening technologies. Based on the structure-permeability paradigm, the Lipinski rule of five has become a standard property filtering protocol for VS. Three possible VS scenarios with respect to optimizing binding affinity and pharmacokinetic properties are discussed. The parsimony principle for selecting candidate leads for further optimization is advocated.

Choosing the Right Molecule 

• Goal: To find a lead compound that can be optimized to give a drug candidate

− Optimization: using chemical synthesis to modify the lead molecule in order to improve its chances of being a successful drug.

• The Challenge: Chemical space is vast

− Estimates vary

• Reymond et al. suggest there are ~1 billion compounds with up to 13 heavy atoms

• There are ~65 million known compounds (example UniChem, PubChem)

• A typical pharmaceutical compound collection contains ~1-5 million compounds

• High throughput screening allows large (up to 1 million) numbers of compounds to be tested

– But very small proportion of “available” compounds

– Large scale screening is expensive

– Not all targets are suitable for HTS

The basic goal of the virtual screening is the reduce on of the enormous virtual chemical space, to a manageable number of the compounds that would inhibit a target protein responsible for disease and also have highest chance to lead to a drug candidate.

Virtual Screening Depending Upon Structural and Bioactivity

Data Available :

• One or more actives molecule known perform similarity searching.

• Several active known try to identify a common 3D pharmacophore and then do 3D database search.

• Reasonable number of active and inactive known train a machine learning model.

• 3D structure of protein known use protein ligand docking

Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) is the prediction of binders to target proteins through computational methods, using the known 3D structure of these targets. The basic approach in SBVS is to predict the binding pose of each small molecule in a test library (docking), and from that predict the free energy of binding of that molecule (scoring).

Medicilon’s virtual screening can reduce clients’ costs and increase hit rates for lead discovery by eliminating the need for robotics, reagent acquisition or production and compound storage facilities. The increased robustness of computational algorithms and scoring functions, the availability of affordable computational power and the potential for timely structural determination of target molecules have provided new opportunities for virtual screening and made it more practical.

Contact Us 

Email : marketing@medicilon.com.cn
Tel : +86 021 58591500

Tips:  Above is part of virtual screening in drug discovery and virtual drug screening. You can also CONTACT US with any question or enquiry you may have. We will be happy to discuss your needs in detail and design an appropriate plan of action.