Tag Archives: code

Viewing fragment elaborations in RDKit

As a reasonably new RDKit user, I was relieved to find that using its built-in functionality for generating basic images from molecules is quite easy to use. However, over time I have picked up some additional tricks to make the images generated slightly more pleasing on the eye!

The first of these (which I definitely stole from another blog post at some point…) is to ask it to produce SVG images rather than png:

#ensure the molecule visualisation uses svg rather than png format
IPythonConsole.ipython_useSVG=True

Now for something slightly more interesting: as a fragment elaborator, I often need to look at a long list of elaborations that have been made to a starting fragment. As these have usually been docked, these don’t look particularly nice when loaded straight into RDKit and drawn:

#load several mols from a single sdf file using SDMolSupplier
#add these to a list
elabs = [mol for mol in Chem.SDMolSupplier('frag2/elabsTestNoRefine_Docked_0.sdf')]

#get list of ligand efficiencies so these can be displayed alongside the molecules
LEs = [(float(mol.GetProp('Gold.PLP.Fitness'))/mol.GetNumHeavyAtoms()) for mol in elabs]

Draw.MolsToGridImage(elabs, legends = [str(LE) for LE in LEs])
Fig. 1: Images generated without doing any tinkering

Two quick changes that will immediately make this image more useful are aligning the elaborations by a supplied substructure (here I supplied the original fragment so that it’s always in the same place) and calculating the 2D coordinates of the molecules so we don’t see the twisty business happening in the bottom right of Fig. 1:

Continue reading →

Out-of-distribution generalisation and scaffold splitting in molecular property prediction

The ability to successfully apply previously acquired knowledge to novel and unfamiliar situations is one of the main hallmarks of successful learning and general intelligence. This capability to effectively generalise is amongst the most desirable properties a prediction model (or a mind, for that matter) can have.

In supervised machine learning, the standard way to evaluate the generalisation power of a prediction model for a given task is to randomly split the whole available data set X into two sets – a training set X_{\text{train}} and a test set X_{\text{test}}. The model is then subsequently trained on the examples in the training set X_{\text{train}} and afterwards its prediction abilities are measured on the untouched examples in the test set X_{\text{test}} via a suitable performance metric.

Since in this scenario the model has never seen any of the examples in X_{\text{test}} during training, its performance on X_{\text{test}} must be indicative of its performance on novel data X_{\text{new}} which it will encounter in the future. Right?

Continue reading →

Trying out some code from the Eighth Joint Sheffield Conference on Chemoinformatics: finding the most common functional groups present in the DSPL library

Last month a bunch of us attended the Sheffield Chemoinformatics Conference. We heard many great presentations and there were many invitations to check out one’s GitHub page. I decided now is the perfect time to try out some code that was shown by one presenter.

Peter Ertl from Novartis presented his work on the The encyclopedia of functional groups. He presented a method that automatically detects functional groups, without the use of a pre-defined list (which is what most other methods use for detecting functional groups). His method involves recursive searching through the molecule to identify groups of atoms that meet certain criteria. He used his method to answer questions such as: how many functional groups are there and what are the most common functional groups found in common synthetic molecules versus bioactive molecules versus natural products. Since I, like many others in the group, are interested in fragment libraries (possibly due to a supervisor in common), I thought I could try it out on one of these.

Continue reading →

OPIG Algorithm Club Problem #1: The 3n+1 problem

In the first meeting of the OPIG Algotirhm Club, we tackled the problem 3n+1 from the Sphere Online Judge (SPOJ).

 —Background —

The problem relates to the Collatz conjecture. Before describing the conjecture, let us define a very simple recursive relation:

  • If a number is odd, multiply it by 3 and add 1 (hence 3n+1).
  • If a number is even, divide it by 2.

The Collatz conjecture states that for any given integer, you can repeat this recursion indefinetely and you will always reach the number 1.

The 3n+1 problem requires yet another concept which is the cycle length (i.e. the number of times you have to repeat the recursion for a given number until you reach 1).

— Goal —

The aim of the problem is: given 2 integers i and j, find the number with the highest cycle length that lies in the interval comprised between the 2 integers (them included).

— Important Issues —

Two things are worth mentioning before attempting to implement this problem:

  •  i could be either greater than or lesser than j.
  • Despite the fact that the description of the problem states that all operations can be performed using int, there are certain inputs between 100,000 and 1,000,000 that violate that statement. 

— Implementation —

A simple solution can be implemented by using a recursive function to compute the cycle length:

int Cycle_Length (unsigned int n) /* Note the unsigned precision */
{
    	unsigned int aux;

        /* Stopping Condition - Don't want the recursion to run for forever */
        if (n == 1) return 1;

        /* Here is the recursion, if n is odd  else     if n is even   */
	    aux = (n%2) ? (1 + Cycle_Length(3*n+1) ) : (1+Cycle_Length(n>>1));
        /* Note that division by two is performed by shifitng a bit to the right */
	    return aux;
}

Now, there are two ways we can optimise this function and make it more efficient.

The first one relates to a certain property of odd numbers: we know that if n is odd, than 3*n+1 is going to be even. Therefore we can simply skip one iteration of the cycle:

aux = (n%2) ? (2+Cycle_Length((3*n+1)>>1)) : (1+Cycle_Length(n>>1)) ;

We can further optimise our code by using Dynamic Programming. We can store the Cycle Lengths we already computed so we don’t need to compute them ever again.

Using dynamic programming and the tricks above, we can get to the final solution (which runs pretty fast on the Online Judge platform):

#include<stdio.h>
#include<stdlib.h>

int CycleLength[100001];

int Cycle_Length (unsigned int n)
{
	int aux;
	if(n>100000)
	{
		aux = (n%2) ? (2+Cycle_Length((3*n+1)>>1)) : (1+Cycle_Length(n>>1)) ;
		return aux;
	}
	if(!CycleLength[n]) 	
		CycleLength[n] = (n%2) ? (2+Cycle_Length((3*n+1)>>1)) : (1+Cycle_Length(n>>1)) ;
	return CycleLength[n];
}

int findmax(int a, int b)
{
	int max=0,aux,i;
	for(i=a;i<=b;i++)
	{
		aux=Cycle_Length(i);
		if(max<aux)
			max=aux;
	}
	return max;	
}

int main()
{
	int a,b;
	CycleLength[1]=1;
	while(scanf("%d %d",&a,&b)!=EOF)
		a<b?printf("%d %d %d\n",a,b,findmax(a,b)):printf("%d %d %d\n",a,b,findmax(b,a));
	return 0;
}

Ta daam!