Practical Cheminformatics Index

AI in Drug Discovery - Please Stop Fishing in the Bathtub!

9 minute read

Published: February 07, 2026

The Hype Cycle and the Reality of Virtual Screening

Practical Cheminformatics with Marimo

7 minute read

Published: January 17, 2026

Introduction
As someone working with drug discovery data, I frequently need a view that provides an overview and then quickly switches to a detailed perspective. I’ve long been frustrated by most software tools’ inability to provide this kind of view. Most systems for processing drug discovery data adopt a spreadsheet view that places compounds in rows and assays in columns. While spreadsheet views can be powerful, they often don’t enable users to quickly switch between summary and detailed views, which are critical for understanding SAR. Read more

Can Gemini Search the ChEMBL Database?

6 minute read

Published: January 09, 2026

Introduction
I’ve been an enthusiastic user of the ChEMBL database for more than a decade. ChEMBL contains a treasure trove of information curated from medicinal chemistry journals. When I’m building an initial machine learning model or analyzing the SAR around a particular target, ChEMBL is the first place I go. I’m reasonably good at SQL, but I still spend a lot of time figuring out which data is in which tables and how to join them correctly to get the information I need. For years, I’ve longed for a more straightforward way to query ChEMBL. I recently discovered that the Gemini CLI from Google can write SQL queries for ChEMBL and extract the information I need into a CSV file. Is this my dream come true? Can I stop spending time staring at the ChEMBL schema and just pose a simple natural-language query like this?

> get the smiles,chembl_id, target_name, publication year, article doi,
and IC50 for all kinase inhibitors published after 2022
and write this into a file called kinase_inhibitors_after_2022.csv

Performing Exploratory Data Analysis on the OpenADMET ExpansionRx Blind Challenge Dataset

3 minute read

Published: November 08, 2025

When working with a new dataset, many people quickly jump into building a machine learning model. I prefer to start with exploratory data analysis (EDA) to gain a deeper understanding of the data. To address this need, I created a notebook that performs initial EDA on the OpenADMET ExpansionRx Blind Challenge Dataset. Instead of using Jupyter for this analysis, I’m using marimo, a new open source data science notebook environment that enables the creation of interactive data apps with minimal code. I think of marimo as a “better Jupyter” because it offers several features that simplify building interactive data apps, including built-in support for Altair charts, an enhanced table view, and interactive widgets. I’m working on a repository titled “Practical Cheminformatics with marimo,” which demonstrates some ways to use marimo for cheminformatics tasks. This code should be ready in a couple of weeks. Please consider this notebook a preview of what’s to come. For those interested in learning more about marimo, I recommend starting with the following resources. Read more