# bd mol Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/bd-mol
> Markdown URL: https://aitinkerers.org/technologies/bd-mol.md
> Technology record last updated: 2026-04-17T20:13:29Z
> Generated: 2026-09-21T19:48:30Z

A deep learning framework for generating drug-like molecules using bidirectional SMILES modeling.

BDMol leverages a bidirectional Transformer architecture to optimize molecular design through chemical language modeling. By processing SMILES strings from both directions, the system captures complex structural dependencies that unidirectional models miss. It excels in scaffold hopping and lead optimization, producing valid chemical structures with high drug-likeness scores (QED &gt; 0.80) and targeted synthetic accessibility. The framework is built on PyTorch and integrates seamlessly with RDKit for rapid property prediction and library generation.

- Official technology site: https://github.com/XinhaoLi74/BDMol
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [/spawn-team: A Self-Healing Multi-Agent Coding Pipeline with Claude Code + Beads](https://seattle.aitinkerers.org/talks/rsvp_yI3yt6tcSig)

A workflow that decomposes a software feature into parallel implementation arms, farms each arm to a Codex agent running in an isolated git worktree, enforces completion ordering through a persistent DAG, auto-retries failed arms with exact judge findings, and merges clean — all resumable across session breaks. The demo shows this running live on a real codebase: pouring the DAG template, watching the explorer dynamically bond implementation arms after reading the code, two Codex agents working in parallel worktrees, the judge failing and retrying, then bd graph showing the full live state.

- Event context: AI Dev Tools Track - Seattle — 2026-05-06 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_yI3yt6tcSig

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