HIGH-THROUGHPUT data collection & machine learning

Enabling

More Efficient

Chemical Processes

What we do

For pharmaceutical and agrochemical companies who need to speed-up process development for organic synthesis.



SOLVE Chemistry provides kinetic insights and quantitative yield data to you in a FAIR manner.


Unlike other Al tools, our automated lab collects high-quality quantitative data that powers our proprietary Al models.

Unlock the Full Potential of Your Process Development TEAM

Process Development with Confidence, Powered by Better Data

By quickly providing quantitative experimental data and insights we enable you to make better decisions, sooner, without increasing risk.

Own your data, integrate your workflow

Your reaction data shouldn't be trapped in PDFs or scattered across spreadsheets. We produce FAIR, machine-actionable data that you can download instantly or integrate securely into your existing databases and workflows via our API. Use it in your models today, feed it to your AI tools tomorrow, and maintain full IP control forever.

Find your optimal reaction solvent in weeks, not months

We deliver data-driven complete response surfaces in 2 weeks instead of 3 months. Informed by computational and proprietary machine learning models, our technology provides you with insights into how the solvent environment affects your reaction. Understand the trade-offs of your solvent choice whether that is yield, cost, or sustainability, with data to back it up.

De-risk your process development with  micro-kinetic modeling

Get intrinsic scale-independent kinetic models, free from mass and heat transfer effects, that tell you exactly how your chemistry will behave. 50× faster than standard kinetic modeling approaches — quantitative kinetic constants, robust kinetic models, purpose-fit for your workflow.

Scale-up right first time

Visualize yield and purity response surfaces and kinetic models, then get the data you need in formats that plug directly into your reactor models, so you can confidently predict performance at scale before you commit to equipment or production runs.Our product integrates with popular reactor modelling software providers.

Backed by

industrial outcomes

How have we unblocked

value for customers?

Data Availability

Fully AI-Ready data collection for customers to unlock the power of frontier technology

We have collected comprehensive FAIR datasets for numerous organic reaction classes. Our technology can identify and fill in gaps in proprietary data for our customers, enabling them to predict accurate yields across broad process conditions.

Experimental collection

We collect quantitive data 10x more efficiently than standard HTE workflows

Our technology minimizes the effect of mass and heat transfer revealing the scale independent chemistry of your process. Our automated lab utilizes on-line UHPLCs to print accurate concentration data rapidly.

Modeling Accuracy

End-to-end automated insight generation and model building

We generate robust chemical insights automatically through interpretable machine learning and micro-kinetic modelling. This allows process development teams to make decisions sooner and with more confidence.

In the news

Society of Chemical Industry

From PhD to CEO: Data driven chemistry with SOLVE

Renaissance Philanthropy

The Science of Scale-Up

Chemical & Engineering News

SOLVE uses new chemical process techniques and machine learning to create efficient processes

UK Research and Innovation

Spin-out SOLVE to digitally transform chemical manufacturing

About Solve CHEMISTRY

At SOLVE Chemistry, we are driven by a shared belief that smarter, data-driven solutions can transform the chemical industry and make a real difference in people's lives. Our mission goes beyond simply optimizing processes: we are committed to empowering our clients with flexible, evidence-based options that allow them to apply their expertise in ways that have the greatest impact.

Every member of our team is dedicated to the idea that by improving efficiency and reducing risk, we can help lower the cost of vital products, from pharmaceuticals to agrochemicals, all while driving innovation and setting a new standard for sustainability.

Our

Company

Founders & Directorship

Dr Linden Schrecker
Founder & CEO
Dr Jose Pablo Folch
CO-Founder & CSO
Alexander Giles
Director

Team

Dr Sarah Boyall
Technical Lead
Thomas Dixon
Technical Lead
Vishal Raman
Founder's Associate
Dr Matthew Bone
Principal Computational Chemist
Dr Nathan Barrow
Director of Product & Customer Strategy
Dr Alexander Atkins
Synthetic Development Lead
Dr Kallum Mehta
Automation Scientist
Amy Zhang
System Chemist
Andrew Reza
Lead Software Engineer
Adrian Martens
Machine Learning Intern
Dr Jesús Sanjosé-Orduna
Research Associate in Continuous Flow Processes
Marco Sandrin
Research Assistant in Optimisation and Design of Experiments

Advisory Board

Prof. Mimi Hii
Advisor, Professor of Catalysis
Prof. Klaus Hellgardt
Advisor, Professor of Chemical Engineering
Dr Christian Holtze
Advisor, Academic Partnership Developer
Dr Joachim Dickhaut
advisor, Senior Principal Scientist

Publications

Selective and Sustainable Oxidation of Allyl Alcohol in Water Using a TS-1 Catalyst in Continuous Flow: A Machine-Learning-Driven Approach

12/2025

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning

12/2025

SoDaDE: Solvent Data-Driven Embeddings with Small Transformer Models

12/2025

Flash Thermal Racemization of Chiral Amine in Continuous Flow: An Exploration of Reaction Space Using DoE and Multivariate Transient Flow

02/2025

Transition Constrained Bayesian Optimization via Markov Decision Processes

12/2024

Automated Optimization of a Multistep, Multiphase Continuous Flow Process for Pharmaceutical Synthesis

10/2024

Auto-VTNA: an automatic VTNA platform for determination of global rate laws

09/2024

Operator-free HPLC automated method development guided by Bayesian optimization

06/2024

A comparative study of transient flow rate steps and ramps for the efficient collection of kinetic data

02/2024

An efficient multiparameter method for the collection of chemical reaction data via ‘one-pot’ transient flow

09/2023

Combining multi-fidelity modelling and asynchronous batch Bayesian optimization

02/2023

SnAKe: Bayesian optimization with pathwise exploration

12/2022

Discovery of unexpectedly complex reaction pathways for the Knorr pyrazole synthesis via transient flow

09/2022

Explore what SOLVE Chemistry can

unlock for your process development

Get in touch