Synthetic Sciences Releases OpenScience: An Open-Source, Model-Agnostic AI Workbench for Machine Learning, Biology, Physics, and Chemistry Research

<div class=”os-stages” id=”os-stages”></div>

<div class=”os-main”>
<div class=”os-console”>
<div class=”os-console-bar”>
<div class=”os-dot” style=”background:#e05a5a”></div>
<div class=”os-dot” style=”background:#e0b400″></div>
<div class=”os-dot” style=”background:#76B900″></div>
<span class=”os-mono”>openscience · agent runtime</span>
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<div class=”os-log” id=”os-log”>
<div class=”os-idle”>Choose a model and a goal, then run the loop. The agent streams each step here.</div>
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</div>

<div class=”os-side”>
<div class=”os-card”>
<h4>Skills fired</h4>
<div id=”os-skills”><span class=”os-idle”>Idle</span></div>
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<div class=”os-card”>
<h4>Databases queried</h4>
<div id=”os-dbs”><span class=”os-idle”>Idle</span></div>
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<div class=”os-card”>
<h4>Inline render</h4>
<div class=”os-render” id=”os-render”><div class=”os-empty”>Output appears after the experiment step.</div></div>
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<div class=”os-foot”>
<div class=”os-note”>Illustrative demo. Steps, skills, and renders are simulated to show the OpenScience loop. Real runs depend on the model and data you provide.</div>
<div class=”os-mtp”>Marktechpost</div>
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</div>

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(function(){
var MODELS = [
{id:”claude-opus”, label:”Claude”},
{id:”gpt-5″, label:”GPT”},
{id:”gemini”, label:”Gemini”},
{id:”glm”, label:”GLM”},
{id:”kimi”, label:”Kimi”},
{id:”deepseek”, label:”DeepSeek”},
{id:”local-ft”, label:”Local fine-tune”}
];
var STAGES = [“Literature”,”Hypothesis”,”Code”,”Experiment”,”Analysis”,”Write-up”];

var GOALS = {
ml: {
label:”ML”, agent:”ml”,
desc:”Goal: PEFT fine-tune a small model and report the loss.”,
skills:[“arxiv-search”,”peft-lora”,”trl-sft”,”eval-harness”,”figures-matplotlib”],
dbs:[“arXiv”,”OpenAlex”,”Semantic Scholar”],
lit:”Retrieved 7 arXiv papers on parameter-efficient fine-tuning.”,
hyp:”LoRA rank 16 should match full fine-tune within 2% at lower cost.”,
code:”Wrote train.py using PEFT + TRL SFTTrainer on a 1.3B base model.”,
exp:”Ran 3 epochs; logged train/val loss per step to disk.”,
ana:”Val loss fell 2.41 -> 0.88; LoRA within 1.6% of full fine-tune.”,
write:”Drafted a short report with the loss curve and a methods note.”,
render:”loss”
},
bio: {
label:”Biology”, agent:”biology”,
desc:”Goal: inspect a protein target and propose mutations.”,
skills:[“uniprot-fetch”,”pdb-structure”,”structure-viewer”,”variant-scan”,”figures”],
dbs:[“UniProt”,”PDB”,”Ensembl”],
lit:”Pulled target entry from UniProt and 4 related structures.”,
hyp:”A binding-pocket residue swap may raise ligand affinity.”,
code:”Loaded PDB structure; mapped conserved residues in the pocket.”,
exp:”Rendered the structure inline and scored 5 candidate mutations.”,
ana:”Two mutations improved the predicted pocket score; logged provenance.”,
write:”Summarized candidates with the structure figure and citations.”,
render:”protein”
},
chem: {
label:”Chemistry”, agent:”research”,
desc:”Goal: screen small molecules for a target activity.”,
skills:[“chembl-query”,”pubchem-fetch”,”rdkit-filter”,”admet-flags”,”figures”],
dbs:[“ChEMBL”,”PubChem”,”arXiv”],
lit:”Queried ChEMBL for known actives against the target.”,
hyp:”Scaffold with a polar substituent should keep activity, lower logP.”,
code:”Wrote an RDKit filter over 1,200 candidate SMILES.”,
exp:”Applied activity and ADMET filters; ranked the survivors.”,
ana:”Kept 38 candidates; top scaffold plotted by activity vs logP.”,
write:”Produced a ranked table and an activity plot with sources.”,
render:”molecule”
}
};

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schedule(function(){ if(g.dbs[2]) chip(dbsEl,g.dbs,2); },t+=step);

schedule(function(){ setStage(1); line(“<span class=’t’>[02] hypothesis</span> “+g.hyp); },t+=step);
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schedule(function(){ setStage(5); line(“<span class=’t’>[06] write-up</span> “+g.write); },t+=step);
schedule(function(){
setStage(6);
line(“<span class=’ok’>✓ loop complete</span> <span class=’t’>· session + provenance saved to disk</span>”);
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// —- inline SVG renders —-
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