Achira develops atomistic foundation simulation models designed to advance drug discovery processes. The company integrates geometric deep learning, physics, and quantum chemistry to create advanced potentials and generative models. This approach generates large, accurate synthetic datasets, enabling generative models to treat drug discovery as an inverse design problem.
Funding
$33M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current biomolecular simulation tools are constrained by limited physics fidelity and cannot fully exploit modern high‑performance computing resources, while pure machine‑learning approaches suffer from scarce, expensive experimental data. This bottleneck reduces the accuracy and throughput of computational drug discovery pipelines.
Solution
Achira creates atomistic foundation simulation models that integrate geometric deep learning, quantum chemistry, and statistical‑mechanics principles into advanced potential energy functions. These models generate large, high‑quality synthetic datasets free of experimental artifacts, enabling generative algorithms to treat drug discovery as an inverse design problem. By leveraging the exponential growth in compute power, the platform delivers quantum‑accurate predictions at scale, accelerating lead identification and optimization for pharmaceutical programs. The solution is delivered via a cloud‑based inference service and an API that can be embedded into existing computer‑aided drug design (CADD) workflows.
Target Audience
Primary customers are pharmaceutical R&D teams, biotech companies, and contract research organizations that run computational chemistry and virtual screening workflows, as well as CADD platform providers seeking next‑generation simulation capabilities.
Features
- Atomistic simulation engine built on geometric deep learning potentials that capture both local chemistry and long‑range interactions.
- Quantum‑level accuracy through integration of ab‑initio quantum chemistry calculations into the training pipeline.
- Statistical‑mechanics framework that ensures thermodynamic consistency across conformational ensembles.
- Automated generation of synthetic molecular datasets at billions of conformations, eliminating experimental bias.
- Generative modeling interface that enables inverse design of compounds targeting specific physicochemical or biological properties.
- Scalable cloud inference service with GPU acceleration, supporting high‑throughput virtual screening.
- RESTful API and Python SDK for seamless integration with existing CADD platforms, molecular dynamics packages, and workflow managers.
- Built‑in compliance with data‑security standards (e.g., encryption in transit and at rest) for proprietary compound libraries.