Working With Drug Data from the ChEMBL Database

When working on drug discovery projects, it's handy to have access to a set of chemical structures and associated data for marketed drugs. If you're considering introducing new functionality, someone invariably asks whether that functionality has been used in a marketed drug. It's also helpful to compare the properties of a new compound or compounds to those of marketed drugs. Early in my career, I remember a new medicinal chemist asking Josh Boger, the founder of Vertex Pharmaceuticals, what they should do on their first day of work. Boger responded, "read the Merck Index so you can see what a drug is supposed to look like". Recently a few papers have been published showing how the properties of drugs have changed over time. I thought it might be helpful to create a notebook showing how to extract and clean drug data from ChEMBL and use it for subsequent analysis.


The Jupyter notebook is available here on GitHub and can also be run here on Google Colab. In the notebook, we go through the following steps.

  1. Download the ChEMBL database.
  2. Query ChEMBL for drug data.
  3. Remove duplicates from the ChEMBL data.
  4. Divide the ChEMBL data into three groups based on the first approval date, before 1997, 1997-2017, and after 2017.
  5. Compare the molecular weight and calculated logP distributions for the three groups and determine if the differences between groups are statistically significant.

Ultimately, we generate boxplots comparing calculated drug properties over time and heatmaps that indicate whether the differences are statistically significant. There are also a few data analysis tricks and tips that some might find helpful. I hope others will learn from this and use this notebook as a foundation for their work. 









Comments

Post a Comment

Popular posts from this blog

We Need Better Benchmarks for Machine Learning in Drug Discovery

Image
Most papers describing new methods for machine learning (ML) in drug discovery report some sort of benchmark comparing their algorithm and/or molecular representation with the current state of the art.  In the past, I’ve written extensively about statistics and how methods should be compared .  In this post, I’d like to focus instead on the datasets we use to benchmark and compare methods.  Many papers I’ve read recently use the MoleculeNet dataset, released by the Pande group at Stanford in 2017, as the “standard” benchmark.   This is a mistake.  In this post, I’d like to use the MoleculeNet dataset to point out flaws in several widely used benchmarks.  Beyond this, I’d like to propose some alternate strategies that could be used to improve benchmarking efforts and help the field to move forward.   To begin, let’s examine the MoleculeNet benchmark, which to date, has been cited more than 1,800 times.   The MoleculeNet collection consists of 16 datasets divided into 4 categories.  Qua
Read more

AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

Image
Here’s the first part of my review of some interesting machine learning (ML) papers I read in 2023.  As with the previous editions , this shouldn’t be considered a comprehensive review.  The papers covered here reflect my research interests and biases, and I’ve certainly overlooked areas that others consider vital.  This post is pretty long, so I've split it into three parts, with parts II and III to be posted in the next couple of weeks.    I. Docking, protein structure prediction, and benchmarking II. Large Language Models, active learning, federated learning, generative models, and explainable AI III. Review articles 2023 was a bit of a mixed bag for AI in drug discovery.  Several groups reported that the deep learning methods for protein-ligand docking weren’t quite what they were initially cracked up to be.  AlphaFold2 became pervasive, and people started to investigate, with mixed success, the utility of predicted protein structures.  There were reports of significant advanc
Read more

Getting Real with Molecular Property Prediction

Image
Introduction If you believe everything you read in the popular press, this AI business is easy. Just ask ChatGPT, and the perfect solution magically appears. Unfortunately, that's not the reality. In this post, I'll walk through a predictive modeling example and demonstrate that there are still a lot of subtleties to consider. In addition, I want to show that data is critical to building good machine learning (ML) models. If you don't have the appropriate data, a simple empirical approach may be better than an ML model.  A recent paper from Cheng Fang and coworkers at Biogen presents prospective evaluations of machine learning models on several ADME endpoints.  As part of this evaluation, the Biogen team released a large dataset of measured in vitro assay values for several thousand commercially available compounds.  One component of this dataset is 2,173 solubility values measured at pH 6.8 using chemiluminescent nitrogen detection (CLND), a technique currently consider
Read more