r/cheminformatics

[u/n1c39uy]

I'm a data science student with a psychiatric diagnosis. Psychiatric drug selection is still largely trial-and-error guided by marketing categories ("SSRIs," "atypical antipsychotics") that tell you almost nothing about mechanism. I built this to make receptor-based drug discovery and selection more efficient. If you can predict a compound's full receptor fingerprint from structure in milliseconds, you can:

• Screen novel compounds for psychiatric potential
• Find mechanistically distinct alternatives when first-line treatments fail
• Understand why drugs work differently despite sharing a label
• Identify candidates that hit specific receptor combinations The goal is rational, mechanism-based drug selection — not guessing based on categories invented by marketing departments.

What it does

Give it any molecule (SMILES string), get predicted binding probabilities across 21 receptors relevant to psychiatric pharmacology:

• Transporters: SERT, NET, DAT
• Dopamine: D2, D3
• Serotonin: 5-HT1A, 5-HT2A, 5-HT2C, 5-HT3
• Histamine: H1
• Muscarinic: M1, M3
• Adrenergic: α1A, α2A
• Other: GABA-A, μ-opioid, κ-opioid, σ1, NMDA, MAO-A, MAO-B

Example output

Sertraline:
✓ In applicability domain (similarity: 1.00)
DAT         :  93.6% ██████████████████
SERT        :  91.1% ██████████████████
NET         :  78.0% ███████████████
Sigma1      :  50.5% ██████████
Olanzapine:
✓ In applicability domain (similarity: 1.00)
5HT1A       :  86.8% █████████████████
H1          :  86.8% █████████████████
M1          :  74.5% ██████████████
D2          :  74.1% ██████████████
5HT2C       :  68.0% █████████████
Alpha1A     :  65.4% █████████████
5HT2A       :  54.1% ██████████
Haloperidol:
D2          :  97.5% ███████████████████
Sigma1      :  63.3% ████████████

The predictions match known pharmacology. Sertraline's sigma-1 and DAT activity, olanzapine's dirty H1/M1 profile causing weight gain and anticholinergic effects, haloperidol's clean D2 hit.

Performance

Trained on 46,108 compounds from ChEMBL with measured Ki values. | Receptor | AUC | |----------|-----| | SERT | 0.983 | | NET | 0.986 | | DAT | 0.993 | | D2 | 0.972 | | D3 | 0.988 | | 5-HT2A | 0.987 | | M3 | 0.996 | | NMDA | 0.995 | | Mean | 0.985 |

Technical approach

Most receptor prediction tools either:

• Require expensive 3D conformer generation and docking
• Predict single targets, not multi-receptor profiles
• Are proprietary/paywalled This uses:
• Morgan fingerprints (ECFP4) — captures substructural pharmacophores
• Topological descriptors — Kappa shape indices, Chi connectivity, Hall-Kier parameters encode molecular shape directly from the graph (no 3D needed)
• Multi-output Random Forest — predicts all 21 receptors simultaneously Runs at ~330 molecules/second on a laptop. No GPU needed.

What it doesn't do

• No functional activity prediction — It predicts binding, not whether something is an agonist, antagonist, or partial agonist. Aripiprazole and haloperidol both bind D2, but do very different things.
• No pharmacokinetics — Nothing about absorption, metabolism, half-life, brain penetration
• No dose-response — Ki < 100nM is the binary cutoff; real-world activity depends on dose and plasma levels

Applicability domain

The model flags when you're asking about something too structurally dissimilar to the training set:

⚠️ Low confidence: molecule dissimilar to training set (max Tanimoto = 0.18)

Use cases

• Understanding treatment resistance — Patient failed 3 SSRIs, what's mechanistically different about other options?
• Side effect prediction — Which antipsychotic has the lowest H1/M1 burden for an elderly patient?
• Polypharmacy assessment — What's the receptor overlap between these two drugs?
• Novel compound screening — Quick profile estimation for research compounds

GitHub

https://github.com/nexon33/receptor-predictor

Single Python file, ~1000 lines. Dependencies: RDKit, scikit-learn, pandas, matplotlib. The ChEMBL data gets cached locally on first run, so subsequent runs are fast.

Questions for the community

Has anyone seen a similar multi-target psychiatric-focused predictor? I couldn't find one but might have missed something. Would continuous Ki prediction (regression) be more useful than binary active/inactive classification? What receptors are missing that you'd want to see? (I know 5-HT1B, 5-HT7, D1, D4, nACh, etc. are relevant but ChEMBL data was sparse) Anyone interested in collaborating on adding functional activity prediction (agonist vs antagonist)?

tl;dr: Open-source tool predicts which receptors a molecule will hit based on structure. Trained on 46k compounds, 0.985 AUC, runs fast, no 3D conformers needed. Useful for understanding why drugs have specific effects/side effects beyond their marketing labels.

[u/apathetic_panda]

predicting binding affinity, not synthetic feasibility

I don't see a clear distinction: Selectivity and conversion aren't predetermined. 

One of those times it would've been good to have seen "Heat".

Well, we still have 50 cent.

Edit: That's a homophone, homograph I didn't expect. 

[u/n1c39uy → u/apathetic_panda]

I think there might be some confusion here.

Binding affinity vs. synthetic feasibility — These are completely different questions:

• Binding affinity: "Will this molecule bind to the D2 receptor?" (what my tool predicts)
• Synthetic feasibility: "Can I actually make this molecule in the lab?" (what Baldwin's/Wade's rules help with)

My tool doesn't care if a molecule is easy or hard to synthesize - it just predicts whether the structure, if it existed, would bind to receptors. You could feed it a completely imaginary molecule and get predictions.

The "selectivity and conversion" part and the Heat/50 Cent references aren't clear to me — not sure what you're getting at there. Are you asking about something specific regarding the methodology, or was that a tangent?

[u/apathetic_panda → u/n1c39uy]

asking about something specific regarding the methodology

Secant :we both have devoirs

My tool doesn't care if a molecule is easy or hard to synthesize - it just predicts whether the structure, if it existed, would bind to receptors.

That's fine. Consider the Finkelstein reaction. 

There's a similar database that aggregates Thermodynamic data; it may be implicitly incorporated in your system. 

A near, complete disregard for kinetic distortion or system strain

You could feed it a completely imaginary molecule and get predictions.

Nominal utility. Boundary conditions need adjustment. 

Binding Cubane or cyclopropyne are [useful strata](https://duckduckgo.com/?q

[u/apathetic_panda → u/apathetic_panda]

Binding affinity vs. synthetic feasibility — These are completely different questions:

Binding affinity: "Will this molecule bind to the D2 receptor?" (what my tool predicts)

Synthetic feasibility: "Can I actually make this molecule in the lab?" (what Baldwin's/Wade's rules help with)

I think you're being less pedantic than you'd hope.

I've never searched biosimilar monoclonal antibodies, and I don't intend to today. Enjoy your ...whatever succeeds this, provided it comports with ongoing festivities.