Abstract
Language | English |
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Publication status | Published - 10 Aug 2014 |
Event | 248th ACS National Meeting & Exposition - California, San Francisco, United Kingdom Duration: 10 Aug 2014 → 14 Aug 2014 http://www.acs.org/content/acs/en/meetings/fall-2014.html |
Conference
Conference | 248th ACS National Meeting & Exposition |
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Country | United Kingdom |
City | San Francisco |
Period | 10/08/14 → 14/08/14 |
Internet address |
Fingerprint
Keywords
- integral equation theory
- RISM
- reference interaction site model
- data mining
- cheminformatics
- support vector machine
- random forest
- solubility
- ADMET
- drug discovery
- hydration free energy
- caco-2
Cite this
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Is there a role for the integral equation theory of molecular liquids in chemical informatics? / Palmer, David; Fedorov, Maxim.
2014. 248th ACS National Meeting & Exposition, San Francisco, United Kingdom.Research output: Contribution to conference › Speech
TY - CONF
T1 - Is there a role for the integral equation theory of molecular liquids in chemical informatics?
AU - Palmer, David
AU - Fedorov, Maxim
PY - 2014/8/10
Y1 - 2014/8/10
N2 - We propose a fast and efficient method to compute molecular descriptors based on the Integral Equation Theory (IET) of Molecular Liquids. The new RISM-MOL-INF descriptors are obtained from solute-solvent correlation functions computed by the 1D Reference Interaction Site Model (RISM). The RISM-MOL-INF descriptors are conceptually different from those commonly used in chemical informatics, since they fingerprint solutes by the change they induce in solvent density distribution, rather than by the solute molecular structure alone. It is shown that key solvation thermodynamic and ADME properties can be predicted accurately using machine learning algorithms trained on RISM-MOL-INF descriptors only. Since they can be evaluated in a matter of minutes for most small organic and druglike molecules, the RISM-MOL-INF descriptors are suitable for medium to high-throughput in silico screening in applications such as pharmaceutical research and development. Due to the importance of solvation and desolvation effects in biological systems, we believe that the RISM-MOL-INF descriptors will find many applications in biophysical and biomedical property prediction.
AB - We propose a fast and efficient method to compute molecular descriptors based on the Integral Equation Theory (IET) of Molecular Liquids. The new RISM-MOL-INF descriptors are obtained from solute-solvent correlation functions computed by the 1D Reference Interaction Site Model (RISM). The RISM-MOL-INF descriptors are conceptually different from those commonly used in chemical informatics, since they fingerprint solutes by the change they induce in solvent density distribution, rather than by the solute molecular structure alone. It is shown that key solvation thermodynamic and ADME properties can be predicted accurately using machine learning algorithms trained on RISM-MOL-INF descriptors only. Since they can be evaluated in a matter of minutes for most small organic and druglike molecules, the RISM-MOL-INF descriptors are suitable for medium to high-throughput in silico screening in applications such as pharmaceutical research and development. Due to the importance of solvation and desolvation effects in biological systems, we believe that the RISM-MOL-INF descriptors will find many applications in biophysical and biomedical property prediction.
KW - integral equation theory
KW - RISM
KW - reference interaction site model
KW - data mining
KW - cheminformatics
KW - support vector machine
KW - random forest
KW - solubility
KW - ADMET
KW - drug discovery
KW - hydration free energy
KW - caco-2
UR - http://www.acs.org/content/acs/en/meetings/fall-2014.html
M3 - Speech
ER -