Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed

The dynamics of stress p53-Mdm2 network regulated by p300 and HDAC1.

We construct a stress p53-Mdm2-p300-HDAC1 regulatory network that is activated and stabilised by two regulatory proteins, p300 and HDAC1. Different activation levels of [Formula: see text] observed due to these regulators during stress condition have been investigated using a deterministic as well as a stochastic approach to understand how the cell responds during stress conditions. We found that these regulators help in adjusting p53 to different conditions as identified by various oscillatory states, namely fixed point oscillations, damped oscillations and sustain oscillations. On assessing the impact of p300 on p53-Mdm2 network we identified three states: first stabilised or normal condition where the impact of p300 is negligible, second an interim region where p53 is activated due to interaction between p53 and p300, and finally the third regime where excess of p300 leads to cell stress condition. Similarly evaluation of HDAC1 on our model led to identification of the above three distinct states. Also we observe that noise in stochastic cellular system helps to reach each oscillatory state quicker than those in deterministic case. The constructed model validated different experimental findings qualitatively

Stability curve induced by .

<p>Plots of concentration level as a function of for different values of exposure times i.e. 10-100 (at constant value of ). The inset is the enlarged portion of the activated regime. In the curve stabilized and activated regimes are demarcated.</p

dynamics for various levels.

<p>The plots of concentration levels as a function of time in hours for various values: (a) , (b) , (c) , (d) , (e) and (f) respectively at constant value of .</p

Noise contribution on dynamics in stochastic system.

<p>The variation of as a function of time in hours in stochastic system for different values of system size,  = 1, 10, 15, 20, 25, 50 (at constant values of and ).</p

Biochemical network model of stress p53-Mdm2-p300-HDAC1.

<p>The schematic diagram of stress p53-Mdm2-p300-HDAC1 model.</p

Stabilization of in stochastic system.

<p>(a) Plots of concentration levels as a function of for different values of system size, V = 10, 30, 50 and 70 and for different values of  = [10–100] as shown in the four left panels. The insets show the enlarged portions of the activated regimes in each case. (b) Plots of level versus for different V = 10, 30, 50, 70 and for two different values of  = 10 and 100 respectively as shown in two right hand panels.</p