nextup-lab 0.1.0

Drop your experiments in, get the next batch to run. Chem · chem-eng · bio.

nextup-lab

Find the best experimental conditions faster.

nextup-lab picks which experiments to run next when you're optimizing yield, selectivity, EC50 — anything you can measure. Hand it the conditions you've already tested and the candidates you haven't, and it ranks the candidates most likely to beat your current best.

from nextup_lab import suggest

next_batch = suggest(history, candidates, objective="yield", k=4)

→ Try it in Colab · 中文 README

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Why this exists

A single round of experiments — catalysts, solvents, temperatures, substrates — can burn weeks of lab time, and most of the results are mediocre. nextup-lab reads what you've measured so far and ranks every untested condition by its probability of beating your current best, so each next batch is closer to optimal.

It's domain-agnostic: synthesis, formulation, microbial strain screening, process optimization — anywhere a condition is a row in a table.

How the input looks

One CSV. One column is what you measure (yield, conversion, EC50, …). Tested rows have a value; untested rows leave it blank. Everything else is conditions — numbers, category labels, or molecule SMILES, mixed freely.

catalyst	solvent	temp_C	substrate	yield
Pd-A	toluene	80	CC(=O)Oc1ccccc1	73
Ni-B	DMF	100	CC(=O)Oc1ccccc1	12
Pd-A	toluene	100	Cc1ccccc1O
…	…	…	…

Column types are auto-detected; you don't have to label anything.

Quickstart

import pandas as pd
from nextup_lab import suggest

df = pd.read_csv("my_experiments.csv")
history    = df[df["yield"].notna()]
candidates = df[df["yield"].isna()].drop(columns=["yield"])

next_batch = suggest(history, candidates, objective="yield", k=4)
print(next_batch)

next_batch is the top-4 candidate rows, sorted best-first, with a nextup_rank column and a nextup_score column (higher = more confident the row is in the top-5% region of the response). Run those four, fill in the results, call suggest() again. Repeat.

Install

pip install nextup-lab

Python 3.10, 3.11, 3.12, on Linux / macOS / Windows. CPU-only — no GPU required. Typical call is well under a minute on pools of a few hundred candidates.

How it works (briefly)

Bayesian optimization under the hood: fit a surrogate to what you've measured, score every candidate by tail probability against your current best, return top-k. For very small inputs (fewer than 5 tested rows, or fewer than 20 candidates) it falls back to a diversity-spread pick — Bayesian methods underperform random in that regime, so it doesn't pretend otherwise.

The default model was selected from a 5-dataset × 165-config × 64-seed screen on published optimization benchmarks (Doyle Pd, Stevens Ni, Perera Suzuki, DeoxyF Ag, others). The benchmark and selection method are described in the project notes.

License

Proprietary. The package on PyPI is distributed under the license in LICENSE — closed-source binary distribution only. No source code is included in the wheel.