The Milestone-Based Services
We know that drug discovery advances by milestones, not just tasks. Whether you're preparing for a funding round, a partnership meeting, or an internal go/no-go decision, our work is tailored to help you cross those checkpoints with confidence.
Why Our Milestone-Based Approach Works:
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Aligns with your team’s scientific and business goals
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De-risks budgeting with clearly defined deliverables
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Supports staged decision-making and funding cycles
We offer structured yet flexible services
to meet clients wherever they are in the drug discovery pipeline
Step 1: Strategic Alignment
We begin every engagement with a strategy session to align your scientific and business milestones with the most suitable AI-powered tools.
Strategic Alignments
sets clear expectations
build mutual trust.
Step 2: Milestone-Aligned Solutions
Milestone-driven approaches
de-risk the engagement
provides tangible value at each phase.
Rather than offering generic packages, we design customized solutions tailored to your specific needs. Whether your project is driven by grants, investor timelines, or internal R&D gates, we align our work with your critical milestones.
Example Milestones:
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Milestone 1: Prioritize 10 lead candidates with predicted ΔG < –9 kcal/mol
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Milestone 2: Validate structure-activity relationships (SAR) using AI-guided analog generation
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Milestone 3: Recommend top 3 candidates for synthesis and in vitro testing
“You bring the science — we bring the algorithms.”
---AIChemists Consulting
Exemplar Projets
Tailored AI Solutions Across the Pipeline
At Alchemists Consulting, we adapt our AI-powered strategies to fit each client’s unique stage and
scientific goal. Below are four representative project types aligned to different phases of drug discovery.
Example 1: Early Hit Campaign
Client: Spock from Star Trek
Goal: Validate a novel target and identify compelling hit compounds to support early screening or fundraising.
Timeline:
1–2 weeks
Optional Add-ons:
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Binding pocket refinement via MD
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Scaffold novelty analysis
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Investor-friendly figures and summaries
Ideal For:
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Biotech startups applying for SBIR or seed grants
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Academic teams seeking translational validation
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Seed-stage companies building early traction
Milestone 1
Target Dossier
Deliverables: Tractability & binding site report
Technical Details: AI-enhanced literature mining, AlphaFold structure prediction, ChEMBL/PubChem ligand mining
Milestone 2
Virtual Screening
Deliverables: Top 50 ranked compounds
Technical Details: Deep Learning or docking-based virtual screening (DiffDock, Surfdock, PharmacoNet, AutoDock, etc.); tailored ~100k compound library
Milestone 3
Hit List
Deliverables: Annotated report of top hits
Technical Details: Rank-ordered list with novelty scoring, rule-of-5 screening, early ADMET flags
Example 2 : Hit-to-Lead Optimization
Client: Luke Skywalker from Star Wars
Goal: Prioritize lead compounds for synthesis by optimizing potency, diversity, and synthetic feasibility.
Timeline:
2–4 weeks
Optional Add-ons:
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FEP validation of 3–5 finalists
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Retrosynthetic analysis
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Custom lead library design (50–100 diverse analogs)
Ideal For:
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Lean teams without in-house chemoinformatics
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Biotechs launching SAR programs
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Projects entering lead profiling phase
Milestone 1
Analog Generation
Deliverables: 500+ AI-designed analogs
Technical Details: Generative models (Reinvent, LigGPT, MegaSyn) guided by known SAR or scaffold preferences
Milestone 2
Potency Filtering
Deliverables: Prioritized compound set
Technical Details: Graph-based ΔΔG models (DeepDelta), synthetic accessibility scoring, Lipinski/CNS rules
Milestone 3
Lead Set
Deliverables: Top 10 synthesis candidates
Technical Details: Chemotype diversity, selectivity/potency ranking, annotated SAR drivers
Example 3: SAR Learning Loop Acceleration
Client: Neo from The Matrix
Goal: Use AI to extract SAR insights and design better compounds for the next synthesis round.
Timeline:
2-4 weeks
Optional Add-ons:
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Retrosynthetic feasibility scoring
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Model updates after assay results (closed-loop learning)
Ideal For:
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Mid-stage biotechs with assay-ready hits
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Med chem teams optimizing around SAR
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Researchers maximizing insight from small datasets
Milestone 1
Data Analysis
Deliverables: SAR landscape & clustering
Technical Details: Analyze 10–100 known actives with attention-based GNNs (e.g., Chemprop); cluster by binding motifs/fingerprints
Milestone 2
Hypothesis Testing
Deliverables: 20–50 test compound designs
Technical Details: REINVENT or STONED-guided generative design based on attention heatmaps, scaffold hopping, or R-group variation
Milestone 3
Recommendation Set
Deliverables: Top 10 next-round candidates
Technical Details: Rank by predicted activity gain, novelty, diversity; synthesis/testing prioritization provided
Example 4: Preclinical Candidate Selection
Client: Optimus Prime from Transformers
Goal: Refine and rank lead molecules to support IND-enabling decisions.
Timeline:
4~6 weeks
Optional Add-ons:
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MD simulation for binding mode validation
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PK/PD prediction modules
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Investor-ready presentation pack
Ideal For:
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Biotechs preparing for Series A or licensing
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Teams transitioning to preclinical
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CROs supporting candidate nomination decisions
Milestone 1
Potency Refinement
Deliverables: FEP + ML scoring
Technical Details: Relative binding free energy (FEP+), Chemprop or TorchMDNet backup scoring
Milestone 2
Selectivity & Safety
Deliverables: Off-target & ADMET profiles
Technical Details: ML off-target predictions (CNS, kinome, cardiac), toxicophore/hERG screening, BBB permeability flags
Milestone 3
Final Report
Deliverables: IND candidate summary deck
Technical Details: Rank plots, radar charts, synth/kill recommendations, go/no-go support slides