The Drug Design Dashboard
This software was incredibly useful in the running of the coursework exercise in our final year units as SeeSAR was really easy to use for my students.
SeeSAR is easy to use, and allows scientists from different disciplines to explore new design ideas.
Its super-duper fast! Much much faster than any pharmacophore screening I've ever done before!
We are heavy SeeSAR users and our students, undergraduate and postgraduate alike, absolutely love it. Numbers of students interested in computational chemistry increased since we have introduced SeeSAR.
SeeSAR is by far the best idea generator in Medicinal Chemistry.
SeeSAR allowed us to understand a specific halogen substitution pattern crucial for robust activity in our functional assays.
The seamless integration between StarDrop and SeeSAR provides a state-of-the-art drug design and development platform to our very unique high school program.
HYDE really gave this improvement!
For older versions and an elaborated list please visit here
|2C9 pKi||Cytochrome P450 CYP2C9 pKi prediction. Affinity prediction of the compound to bind at the enzyme involved in several metabolic drug pathways.|
|2D6 affinity category||Cytochrome P450 CYP2D6 classification. Compounds are predicted to be in one of four categories: ‘low’ for compounds with a pKi<5, ‘medium for compounds with a pKi between 5 and 6, ‘high for compounds with a pKi between 6 and 7, and ‘very high’ for compounds with a pKi>7.|
|BBB category||Classification of compounds into ‘+’-category if log([brain]:[blood])≥-0.5 and ‘-’category if log([brain]:[blood])<-0.5.|
|BBB log([brain]:[blood])||Logarithm of brain-blood partition coefficient of a compound. Can be used as indicator for CNS active compounds.|
|HIA category||Human intestinal absorption classification. Compound which are predicted to be absorbed ≥30% are classified with ‘+’, compounds which are predicted to be absorbed <30% are classified with ‘-’.|
|P-gp category||P-glycoprotein 1 (also known as multidrug resistance protein 1 (MDR1) and ATP-binding cassette sub-family B member 1 (ABCB1)) transport classification. Predicts if a compound is a substrate of P-gp.|
|PPB90 category||Plasma protein binding classification. Predicts a classification of ‘low’ for compounds which are <90% bound and high for compounds which are >90% bound.|
|hERG pIC50||Prediction of pIC50 values for inhibition of human Ether-a-go-go Related Gene (hERG) potassium channels expressed in mammalian cells.|
|logD||Logarithm of n-octanol-water partition coefficient (also known as n-octanol-water partition ratio) at fixed physiological pH 7.4. Used to describe the relationship between lipophilicity and hydrophilicity of an ionized compound.|
|logP||Logarithm of n-octanol-water partition coefficient. Used to describe the relationship between lipophilicity and hydrophilicity of a neutral compound.|
|logS||Logarithm of intrinsic aqueous solubility in µM for neutral compounds.|
|logS @ pH 7.4||Logarithm of intrinsic aqueous solubility at physiological pH 7.4 in µM for ionized compounds.|