Drug Design 2 - Applied Bioinformatics Group

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AlogP vs. ClogP • AlogP and ClogP yield comparable performance • There are some differences with respect to molecular mass • ClogP slightly be"er for smaller molecules, AlogP for larger ones • Both methods are standard methods in computer aided-‐drug design and are being used widely PredicDon of Other ProperDes Ghose et al., J. Phys. Chem. A (1998), 102, 3762 • pK a values can be predicted in a similar fashion, although performance is worse • Solubility is very difficult to predict • Based on pK a predic%on (ions usually have very nega%ve solva%on free energies) • Solva%on free energy is influenced dras%cally by the lakce energy • Predic%on of lakce energies is very tricky QSPR • Computa%onal predic%on of molecular proper%es is a field of its own within chemoinforma%cs: QSPR – Quan7ta7ve Structure-‐Property Rela7onships • The approaches discussed so far for the predic%on of log P fall into this category • A special case of QSPR is QSAR – Quan7ta7ve Structure-‐ Ac7vity Rela7onships – here we try to predict pharmacological proper%es – in par%cular biological acDvity • Both approaches require an encoding of the structure in a special form (e.g., number of fragments, atoms)
QSPR • Representa%on of a structure by so-‐called descriptors • Descriptors encode proper%es or aspects of the structures as numbers • Based on these numerical representa%ons of the structures, mathema%cal func%ons for the desired property are derived Structure Descriptors Deskriptoren Descriptors Property • Descriptors aim at encoding relevant features of a structure • Simple descriptors are, for example, based on the presence, absence or number of certain funcDonal groups or certain atom types = generaliza%on of fingerprints (! lectures on 2D similarity) • Other descriptors correspond to experimental properDes of a compound, e.g., AlogP or ClogP, refrac%on index, … • Also experimental data of compounds is some%mes used as descriptors – Disadvantage: data is omen incomplete, cannot be computed Descriptor Classes • There are numerous (10,000+) descriptors that have been used in the literature • Many of these descriptors are just minor varia%ons of others • Descriptors can be subdivided into roughly five classes • 0D – Simple counts (e.g., atom counts) • 1D – Fragment counts (2D fingerprints) • 2D – Topological (graph-‐based) • 3D – Geometrical and surface-‐based • 4D – 3D + sampling of conformaDons • Both quality of the descriptors and computa%onal cost increase from 0D to 4D
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: ClogP • ClogP now adds the contri
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 and 20: Drug-‐Likeness • Sadowski et
Page 21 and 22: Drug-‐Likeness • Ajay et al.

Drug Design 2 - Applied Bioinformatics Group

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