Drug Design 2 - Applied Bioinformatics Group

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Drug-‐Likeness • Structures were encoded using the AlogP atom types • Of the 120 atom types of Ghose & Crippen, 92 turned out to be relevant • Each molecule is encoded as a vector containing the number of atoms of each of the 92 types in the structure • This vector then serves as input for the classifica%on into drugs and non-‐drugs COOH O O CH 3 Drug-‐Likeness 0 4 0 2 0 0 0 1 ... 0 • Predic%on based on an arDficial neural network (ANN) • Input: atom type vector • Output: drug-‐likeness (0 = non-‐drug, 1 = drug-‐like) • Topology: 92 x 5 x 1 (simple feed-‐forward ANN) WDI ACD Drug-‐Likeness ANN 83% of ACD and 77% of WDI are classified correctly! Score
Drug-‐Likeness • Ajay et al. chose a slightly different approach to predict drug-‐ likeness • Their comprehensive study uses data from ACD and CMC (Comprehensive Medicinal Chemistry) • They removed all compounds from CMC that are not drugs as such (spermicidal compounds, propellants, …) • Classifica%on was done based on an ar%ficial neural network as well • Descriptors were the 166-‐bit ISIS keys (structural fingerprints) and seven addi%onal descriptors Drug-‐Likeness – Descriptors Ajay et al., J. Med. Chem. (1998), 41, 3314 • ISIS keys contain informa%on on 166 different structural fragments and are very popular for 2D similarity analysis as well • Ajay et al. complemented the structural descriptors with seven descriptors that are most likely relevant for drug-‐likeness: • ClogP • MW (molecular weight) • # H-‐bond donors • # H-‐bond acceptors • # freely rotatable bonds • Aroma%c density (# aroma%c rings/molecular volume) • 2 κ α (a topological index similar to 2 χ) • Note: among these descriptors are also all descriptors relevant for Lipinski’s Rule Drug-‐Likeness – Results • Ar%ficial Neural Network (173 x 5 x 1) • Trained on ACD/CMC • Results: • 90% of CMD classified correctly • 90% of ACD classified correctly • Good generaliza%on • Test with external dataset: MDDR (MACCS-‐II Drug Data Report) • 80% of the MDDR structures were classified correctly
Page 1 and 2: Number of Candidates Overview BIOIN
Page 3 and 4: Reduce AZriDon Rate • „Fail ear
Page 5 and 6: log P and log k • Biological barr
Page 7 and 8: Enteral AbsorpDon Neutral compound
Page 9 and 10: ClogP • ClogP now adds the contri
Page 11 and 12: QSPR • Representa%on of a structu
Page 13 and 14: Wiener Index • In 1947, H. Wiener
Page 15 and 16: Topological Indices • There is a
Page 17 and 18: 3D Descriptors - PSA • A very pop
Page 19: Drug-‐Likeness • Sadowski et

Drug Design 2 - Applied Bioinformatics Group

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