AI-assisted drug discovery workflow v0

Accelerating therapeutic discovery — from concept to validation-ready candidates

Genesis Molecular Engine, Inc. is a pipeline-first discovery interface that helps teams generate, evaluate, and prioritize small‑molecule candidates with scientific rigor.

View Pipeline Technology Stack Partnerships

Repeatable workflow Generate → Screen → Prioritize

Multi-signal scoring Docking + ADMET + novelty

Audit-ready outputs Reports for partners & diligence

Platform Snapshot

A concise view of modules the platform can expose as the roadmap progresses.

Molecule Generation Docking ADMET Novelty Codex Logs AEON Layer

Mission Enable rational candidate selection by integrating computational chemistry signals into a single, auditable narrative: what changed, why it matters, and what decision follows.

[GENESIS.LOG] target=HIV-1 protease score=-9.3 (smina) filters: qed=0.44, alerts=2 action: reduce reactive motifs; preserve binding contacts

What is Genesis? Investor Overview

For external use, replace example metrics with validated results and cite datasets/tools.

About

Genesis is a modular discovery workflow that converts molecule generation into repeatable, auditable decision steps. It supports early discovery today while providing a clear path toward a scalable platform.

Operating Mode: Prototype → Platform

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Compound design

Generate analog families with constraints (scaffolds, warheads, property windows) and capture the rationale for each change.

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Screening signals

Docking + risk flags + novelty similarity checks, consolidated into an explainable prioritization record.

📜

Codex logs

Each candidate receives a “scroll”: metrics, decisions, and next experiments — exportable for partners and investors.

Pipeline

A practical end‑to‑end workflow designed for immediate execution and long‑term scalability.

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Core flow

Target definition Select receptor/PDB, define binding constraints, and choose a measurable success criterion.

Analog generation Generate structurally coherent candidates with design notes and property constraints.

Docking + validation Rank by binding score, then validate pose quality and key interactions.

ADMET triage Filter red flags (e.g., hERG/AMES/DILI) and iterate designs to reduce risk.

Candidate shortlist Export the best set with full tables and rationale for partner review or wet-lab planning.

Outputs

Each run produces artifacts suitable for partner communication and diligence packets.

Candidate table

SMILES, docking scores, property metrics, alerts, novelty similarity, and recommended next action.

Codex report (PDF/HTML)

A narrative report: what changed, why it matters, and the recommended experiments to validate the hypothesis.

Partner summary

Executive-ready brief: program objective, key results, roadmap, and collaboration request.

Note: Replace any placeholder metrics with validated results before external publication.

Technology

A modular stack — start lightweight, then deepen into automated modeling and learning loops as evidence grows.

Meet AEON

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AI generation

Template-constrained generation for analog families, with optional reinforcement on success signals.

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Cheminformatics

Descriptor pipelines for sanitization, alerts, similarity search, clustering, and reporting.

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Docking layer

Pose generation and scoring workflows with reproducible settings and parameter logging.

Quality gates

Filters for PAINS/alerts, rule-of-five, and toxicity predictors — treated as first-class data in decision making.

Export & auditability

Export to PDF/HTML/CSV with timestamps and run parameters to support internal review and partner replication.

AEON Layer

Decision intelligence that turns computational outputs into clear, action-oriented explanations.

AEON: Explain → Recommend → Remember

What AEON does

AEON tracks why each molecular change was made and how it affected outcomes. It summarizes trade-offs (potency vs. safety), proposes next design moves, and keeps an audit trail.

Example AEON insight “Docking improved after increasing hydrophobic bulk, but risk flags increased. Next iteration: reduce reactive motifs, preserve binding contacts, and re-balance polarity into a safer region.”

AEON modules

Design Memory Risk Triage Roadmap Hints Partner Briefs

Optional extensions: dataset-backed confidence scoring and continuous improvement as validated results are incorporated.

Investor Overview

A concise overview: scientific thesis, execution milestones, and how capital accelerates progress.

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Thesis

The fastest path to value is a disciplined, repeatable discovery loop: generate high‑quality analog families, evaluate them across orthogonal signals, and deliver decision‑ready candidate packages suitable for partnership or validation.

Milestones

Quarter	Deliverable
Q1	Validated pipeline demo + exportable reports
Q2	Partner pilots + curated target programs
Q3	Improved scoring + automated learning loop
Q4	Candidate packages for wet lab validation

What funding enables

Concrete, diligence-friendly uses of capital.

Compute & toolingHigher-throughput screening, validation runs, and reproducible infrastructure.

Data partnershipsBenchmarks, curated assays, and external validation pathways.

Team expansionCheminformatics, ML, and business development to ship and commercialize the platform.

Note: Valuation discussions should be supported by demonstrable traction, defensible IP, and comparable market data.

Contact

For partnerships, pilot studies, or investor discussions. This form generates a local email draft.

Email draft (local)

Partnership tracks

Select a track and we will reply with next steps.

Wet-lab validationShortlist candidates + assay plan + data-sharing workflow.

Research collaborationTarget program + joint publication / IP strategy.

Platform licensingCustom workflow + reporting + internal enablement.

Replace placeholders with your official company contact details once finalized.