DRUG DISCOVERY WITHOUT THE HEADACHE
Too many tools, too little interoperability. Cut through license limits, fragile toolchains, and format clashes. The Pauling.AI workflow takes you from a PDB ID or gene name to CRO-ready hits fast.
If you haven’t read it yet, check out my previous blog post:
“IF WET LAB VALIDATION IS ESSENTIAL, WHY USE COMPUTATIONAL METHODS IN DRUG DISCOVERY?” https://blog.pauling.ai/p/if-wet-lab-validation-is-essential.
Do you work in a clinic, a pharmaceutical company, or a biomedical research institution? Then you already know the challenge: moving promising therapies to the next milestone faster, with fewer resources and under tighter budgets. One proven way to accelerate progress is by combining experimental assays with computational methods.
Easier said than done, of course.
The ecosystem of computational chemistry and biology is a maze of thousands of standalone tools, each with its own file formats, parameters and quirks. At the same time, the experts who can stitch it all together (fluent in biology, chemistry, biophysics, and IT) are in short supply. Even simple projects can fragment into dozens of brittle steps where a single format mismatch can stop all pipelines.
Three familiar paths and their limits
You might be leading a department in cancer research, neuroscience, personalized medicine, genetic or infectious diseases. Whichever the case, your options probably look familiar.
1. Wet-lab or CRO screening.
Working with small libraries of a few thousand molecules is manageable because scaling to tens of thousands quickly becomes prohibitively expensive (often millions of dollars).
Reliable, but slow, very low diversity and libraries containing millions of molecules for screening are rarely affordable.
2. Computational research via internal research facilities in your organisation.
These teams are often small, juggling more projects than their computational resources allow. Hardware limits and a small pool of costly commercial software* licenses mean only a few workstations are shared across many research projects. Even with access to a local HPC cluster, license restrictions cap performance to a handful of nodes (tokens or CPUs). The size of a library for virtual screening is usually limited to a million per target in calculations, which takes months on workstations. This way stays shallow and correlates poorly with wet-lab results.
Better than nothing, but still slow and constrained.
3. Basic cloud services.
Most cloud services in drug discovery use academic software* via GUI (graphical user interface), but they don’t solve the core issues. Each academic program still needs to be learned and tuned; most services don’t scale well within a project. You are limited to a single protein–ligand docking instead of screening millions.
Good for teaching, but not for production.
What if the bottlenecks disappeared?
Pauling.AI removes these barriers.
It unifies the computational workflow into a single, production-ready environment that automates the multi-step processes of drug discovery. Format conflicts disappear, protocols stay consistent, and workloads scale seamlessly across thousands of CPUs and GPUs via the cloud-enabling virtual screening of hundreds of millions of compounds with MD simulations in days.
An intelligent agent guides the process from a PDB ID or gene name to a CRO-ready hit list, so your team can focus on wet-lab validation. With full automation and scale, computational and preclinical workflows take days, not months or years (bringing therapies to patients faster than ever or securing patents on drug candidates faster than competitors).
*Academic software is often free but runs from the command line and demands programming skills most biomedical researchers lack. Many tools were designed for single academic problems, not interoperability: formats clash, protocols break, and maintaining a stable toolchain becomes a project in itself. Commercial suites are expensive, slow to evolve, and hard to scale. In both cases, you invest months or years in training staff and maintaining infrastructure.