In chemical concepts, dominant skeleton refers to the core structure of small molecules. In computational chemistry and medicinal chemistry, the skeleton also has the same meaning. The discovery of active compounds with novel skelecton is a great challenge for modern medicinal chemistry. It can not only improve the efficacy and drug-like properties of molecules, but also help pharmaceutical companies break patent barriers and build core competitiveness. Skeleton transition is a commonly used strategy for exploring novel skeleton, which usually starts from known compounds to find compounds with similar activities but with different core structures. Computational chemistry can systematically separate and compare the dominant skeleton of active compounds, and perform computational searches on molecules with similar activities. Commonly used methods for calculating skeleton transitions include pharmacophore search, shape similarity search, chemical similarity and machine learning methods.
Figure 1. Skeletal conformation. (Kimura, A.; et al. 2014)
Dominant skeleton, as the core of the compound structure, can well present the diversity of the compound library. The drug design based on the skeleton structure can be carried out effectively in high-throughput screening. At Alfa Chemistry, we provide various skeleton libraries to screen out dominant skeleton for the discovery of high-potential lead compounds.
The Mini skeleton library is a collection of 5,000 small molecules and established based on the skeleton structure space covered by Chemdiv's 1.6 million small drug molecules. According to the skeleton structure, only one corresponding small molecule is selected for each skeleton.
Each skeleton in the Golden skeleton library has selected two corresponding molecules. The entire collection contains 10,000 small molecules, corresponding to 5,000 skeleton structures. Our Golden skeleton library helps to reveal the SAR relationship and improve the screening hit rate.
Representative core library is designed based on the Bemis-Murcko skeleton concept. First, the REOS, MedChem & PAINS filter is used to remove the 1.6 million small molecules with poor reactivity, poor drug-like properties, high toxicity, and unsatisfactory hybrid activity. A BMS is then generated for each compound and the RDKit MaxMin algorithm (Tanimoto, ECFP4, 2048 bits) is used to perform the cluster analysis for each BMS through the formula to obtain the corresponding small molecules. Finally, a representative core library containing 300,000 small molecules can be obtained.
CBFP is a binary code for predicting biological activity of compound molecules on the QSAR model of candidate drug targets. We have combined multiple machine learning methods (RF, AdaBoost, and GBM) and chemical descriptors (CATS, MACCS, MOE2D) to train classification models for each target data set. We can use CBFP to explore novel skeleton and expand the space of existing medicinal chemistry.
We use virtual screening to generate the type of skeletal transition which focuses on the new core structure. Alfa Chemistry's field technology is applied to find key groups in the molecule for skeleton transition or side face replacement, so that new structures can be identified in the new chemical space. In addition, our scientists can overcome the chemotype trap and find innovative skeleton for the lead in the new chemical space.
Our dominant skeleton discovery and screening services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. 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!