Limits...
Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads.

Reynolds CR, Muggleton SH, Sternberg MJ - Mol Inform (2015)

Bottom Line: PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products.The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016.Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Imperial College London, South Kensington Campus London SW7 2AZ, UK.

ABSTRACT

The use of virtual screening has become increasingly central to the drug development pipeline, with ligand-based virtual screening used to screen databases of compounds to predict their bioactivity against a target. These databases can only represent a small fraction of chemical space, and this paper describes a method of exploring synthetic space by applying virtual reactions to promising compounds within a database, and generating focussed libraries of predicted derivatives. A ligand-based virtual screening tool Investigational Novel Drug Discovery by Example (INDDEx) is used as the basis for a system of virtual reactions. The use of virtual reactions is estimated to open up a potential space of 1.21×10(12) potential molecules. A de novo design algorithm known as Partial Logical-Rule Reactant Selection (PLoRRS) is introduced and incorporated into the INDDEx methodology. PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products. The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016. Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

No MeSH data available.


Line graph showing the number of virtual products as a higher desirability cut-off is used for filtration. Figures averaged over three representative targets (EGFr, COX-2 and P38).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4641463&req=5

fig11: Line graph showing the number of virtual products as a higher desirability cut-off is used for filtration. Figures averaged over three representative targets (EGFr, COX-2 and P38).

Mentions: A preliminary study was conducted into filtering by drug-likelihood. The most well-known measure of drug-likelihood is the “rule of five”.[35] On average, the virtual product molecules 62 % have no violations of Lipinski’s rule of five and 35 % have a single violation, which is allowed by the rule so it has little discrimination power here. More recently, a desirability score[34] has been developed to quantify the drug-likeness of a molecule based on Molecular Weight, LogP, H-bond Acceptors, and H-bond Donors. Desirability can be calculated for each virtual product and used to filter out molecules. Figure 11 shows the decrease in virtual products as a higher desirability cut-off is used. Bickerton[34] found that the mean desirability of approved drugs was 0.492. Setting the cut-off at 0.5 desirability removes 76 % of the virtual products, and a cut-off of 0.7 removes 95 %.


Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads.

Reynolds CR, Muggleton SH, Sternberg MJ - Mol Inform (2015)

Line graph showing the number of virtual products as a higher desirability cut-off is used for filtration. Figures averaged over three representative targets (EGFr, COX-2 and P38).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4641463&req=5

fig11: Line graph showing the number of virtual products as a higher desirability cut-off is used for filtration. Figures averaged over three representative targets (EGFr, COX-2 and P38).
Mentions: A preliminary study was conducted into filtering by drug-likelihood. The most well-known measure of drug-likelihood is the “rule of five”.[35] On average, the virtual product molecules 62 % have no violations of Lipinski’s rule of five and 35 % have a single violation, which is allowed by the rule so it has little discrimination power here. More recently, a desirability score[34] has been developed to quantify the drug-likeness of a molecule based on Molecular Weight, LogP, H-bond Acceptors, and H-bond Donors. Desirability can be calculated for each virtual product and used to filter out molecules. Figure 11 shows the decrease in virtual products as a higher desirability cut-off is used. Bickerton[34] found that the mean desirability of approved drugs was 0.492. Setting the cut-off at 0.5 desirability removes 76 % of the virtual products, and a cut-off of 0.7 removes 95 %.

Bottom Line: PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products.The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016.Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Imperial College London, South Kensington Campus London SW7 2AZ, UK.

ABSTRACT

The use of virtual screening has become increasingly central to the drug development pipeline, with ligand-based virtual screening used to screen databases of compounds to predict their bioactivity against a target. These databases can only represent a small fraction of chemical space, and this paper describes a method of exploring synthetic space by applying virtual reactions to promising compounds within a database, and generating focussed libraries of predicted derivatives. A ligand-based virtual screening tool Investigational Novel Drug Discovery by Example (INDDEx) is used as the basis for a system of virtual reactions. The use of virtual reactions is estimated to open up a potential space of 1.21×10(12) potential molecules. A de novo design algorithm known as Partial Logical-Rule Reactant Selection (PLoRRS) is introduced and incorporated into the INDDEx methodology. PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products. The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016. Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

No MeSH data available.