Practical guides to computational chemistry workflows, method selection, and interpreting outputs for research decisions.
A practical comparison of the resources needed for a computational binding screen versus synthesizing and testing the same compounds in the wet lab. For research leads deciding where in the discovery cycle to introduce in-silico methods.
Read articleHow to plan a large-scale virtual screen, what to review before submission, and how to interpret ranked results without getting buried in noise.
A primer on docking methods, outputs that matter for medicinal chemistry decisions, for teams without an in-house computational scientist.
A walkthrough of one research question: how a PI submits a hypothesis in the afternoon and reviews binding results before the next morning's group meeting.
Why many labs still synthesize before computing, and how the real cost difference changes when measured per compound and per week.
A clear look at computational toxicology: what its predictions cover, where they fall short, and how to use them as a filter instead of a final answer.
How to turn a wet-lab question into a well-defined computational experiment, covering target selection, input preparation, method choice, and output interpretation.
Large pharma groups have in-house computational scientists, while small teams often do not. Here is why the gap can favor smaller groups with the right tooling.
What AI agents can and cannot do in a research workflow: the capabilities available now and the tasks that still need a scientist in the loop.
An introduction to MD outputs for researchers seeing trajectory data for the first time. RMSD, RMSF, signs of stability, and what they do not show.
Early-stage decisions often rely on too little data. This note examines common discovery workflow gaps and the computational methods that can fill them affordably.
A guide for labs with limited budgets and no computational staff. Covers three methods with strong early-stage ROI and how to set realistic expectations for their outputs.
