300k Representative Compounds Library (Bemis-Murcko Clustering Algorithm)

300k Representative Compounds Library (Bemis-Murcko Clustering  Algorithm) 300k Representative Compounds Library (Bemis-Murcko Clustering  Algorithm) 300k Representative Compounds Library (Bemis-Murcko Clustering  Algorithm) 300k Representative Compounds Library (Bemis-Murcko Clustering  Algorithm)
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Desirable size of the custom library selection:
Amount:
Mg
  • Mg
  • uMol
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ChemDiv’s 300k Representative Compounds Library (Bemis-Murcko Clustering Algorithm).

300k diverse selection from ChemDiv 1.6M stock is based on Bemis-Murcko clustering workflow:
1) Generate Bemis-Murcko scaffolds (BMS) for every molecule in the entire 1.6M ChemDiv inventory
2) Apply REOS, MedChem & PAINS filters to remove reactive, toxic, promiscuous, and other undesirable structural motifs
3) Then apply Physico-Chemical Properties filters to remove non-druglike molecules
4) For the remaining selection, generate Bemis-Murcko scaffolds (BMS) for every molecule, calculate the number of unique BMS and number of molecules per each BMS
5) Split the selection into 3 categories for future clustering and diversity picking:
a. Number of individual molecules per unique BMS : 1-3
b. Number of individual molecules per unique BMS : 4-1000
c. Number of individual molecules per unique BMS : >1000
6) Iteratively select (i.e. cluster) individual molecules per each BMS within each category using RDKit MaxMin algorithm (Tanimoto, ECFP4, 2048 bits), cluster size (i.e. number of picked individual molecules per each BMS) calculated by the formula
7) Combine all 3 selection into the final 300k set
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