Target and Concept: Learning Where a Molecule Should Start

📍 Where we are: Part II · Discovery & Development, Learned — Chapter 4. Part I built the foundations: why bioprocess breaks the data-science rulebook, what makes data the binding fuel, and how models are validated under GxP. Now we start walking the spine from its very first node — the choice of what to make, before any of it has to be manufactured.

A manufacturing book that begins at the bioreactor begins too late. Long before WCB-CHO-001 exists, before there is a clone or a process or a single gram of mAb-A, someone chose a biological target to hit, a modality to hit it with, and — implicitly, often carelessly — committed the company to making a particular kind of molecule for years. That commitment is the seed of every manufacturing problem and every manufacturing success downstream. This chapter is about the machine learning that lives at that first node, and about a harder, quieter idea that the rest of Book 5 depends on: manufacturability is a property you choose at concept, not one you discover at scale-up.

We must be honest about scope right away. Most of the AI you read about at "the start of drug development" is drug-discovery AI — protein-structure prediction, generative chemistry, target identification from omics — and it is a vast, fast-moving field of its own that this book does not try to cover. Our lens is narrow on purpose: we care about the slice of concept-stage learning that bears on whether the resulting molecule can be made reliably, at quality, at cost. That slice is small, under-studied, and exactly where the discovery world and the manufacturing world fail to talk to each other.

The simple version

Before an architect draws a skyscraper, someone decides which building to put up and on which plot of land. Choose a swamp and the most brilliant engineering downstream still fights the foundations forever. Drug discovery is the architecture competition — dazzling, and largely someone else's job. This chapter is the site survey: the early, unglamorous question of whether the ground can hold the building you are about to design. A molecule that "scores well" against its biological target but folds badly, aggregates, or cannot be expressed in CHO cells is a tower on a swamp. The cheapest place to learn that is here, at concept — and machine learning is starting to be able to tell you.

What this chapter covers

• The first node of the manufacturing spine: target, mechanism of action (MoA), and modality as the choices that set everything downstream.
• Where drug-discovery AI ends and manufacturing-relevant concept-stage learning begins — an honest boundary, drawn deliberately.
• Target tractability as a prediction problem: how ML scores whether a target is reasonable to drug, and what that has (and has not) to do with manufacturing.
• Manufacturability-by-design: the mindset, the in-silico developability signals available at concept, and why they are weak, sparse, and worth using anyway.
• The discovery → manufacturing handoff gap — a real, well-documented hole in the spine, and what a learned target-profile record would carry across it.
• The anatomy of one manufacturability-aware target profile, the series signature applied to the very first decision.
• The unsolved part: concept-stage manufacturability prediction is the weakest-grounded ML in this whole book, and pretending otherwise is dangerous.

The first node: target, mechanism, modality

Everything in this series — five books, one process — hangs off a spine of unit operations, and that spine has a head. Before molecule discovery generates candidate sequences, before cell-line development picks the clone that becomes WCB-CHO-001, a program must answer three coupled questions:

1. Target — which biological molecule (a receptor, a cytokine, an enzyme) does the disease hinge on, such that engaging it changes outcomes?
2. Mechanism of action (MoA) — how do we want to engage it: block it, degrade it, recruit an immune effector, deliver a payload?
3. Modality — what kind of molecule embodies that mechanism: a monoclonal antibody, a bispecific, an antibody-drug conjugate, a fusion protein, a cell or gene therapy?

For our running example the answers are fixed and familiar: the target is the antigen mAb-A binds, the mechanism is straightforward antigen engagement by an IgG, and the modality is a standard monoclonal antibody made in CHO. That ordinariness is the point. An IgG mAb in CHO is the most manufacturable thing in biologics — a platform with decades of accumulated process knowledge, Protein A capture, well-understood analytics, and a release panel the whole industry shares. The modality choice at concept is why BATCH-2026-001 can reach a monomer purity of 98.611% by SEC at all. Choose a fragile bispecific or a sticky, high-viscosity format instead, and the same downstream machinery struggles. The first node sets the difficulty of every node after it.

This is where Book 5 diverges from a discovery textbook. A discovery scientist asks "will engaging this target help patients?" A manufacturing-minded reader asks a second question the discovery scientist often defers: "and can we make, at quality and scale, the molecule that engages it?" The two questions are not the same, and the machine learning that answers each is different. Book 4 modeled this same head-of-spine node as a knowledge-graph entity — the target and concept as the root of the genealogy; Book 5 asks what can be learned and predicted there.

Drawing the boundary: where discovery AI ends

It would be easy, and wrong, to spend this chapter cataloguing AlphaFold-style structure prediction, generative small-molecule chemistry, and omics-driven target identification. Those are real, they are extraordinary, and they are not manufacturing. Drawing the boundary cleanly is itself a contribution, because the hype routinely blurs it — a press release about an "AI-designed drug candidate" tells you nothing about whether that candidate can be expressed in a 2,000 L bioreactor.

Here is the boundary this book uses. We treat as out of scope (acknowledged, not covered): target identification from genomics/transcriptomics, protein-structure prediction, generative de novo binder design as a discovery activity, and small-molecule cheminformatics. We treat as in scope the concept-stage learning that produces a signal a manufacturing organization can act on:

• Target tractability / druggability — but only insofar as a target's properties constrain the modality, which in turn constrains manufacturability. (A target reachable only by a membrane-spanning multispecific is a manufacturing commitment, not just a biology one.)
• Manufacturability-by-design signals — in-silico predictions, available from sequence or early structure, of properties like aggregation propensity, expressibility, solubility, and viscosity that determine whether a molecule will survive cell culture, purification, and high-concentration formulation.
• The handoff — the information, learned or measured at concept, that must travel down the spine for downstream models to do their jobs, and that today mostly does not.

The next chapter, Molecule Discovery, goes deep on the generative-design-plus-developability-prediction loop once a target is fixed and you are choosing among candidate sequences. This chapter sits one step earlier, where you are choosing the target and modality themselves — and where manufacturability is still a faint signal you can either listen for or ignore.

Target tractability as a prediction problem

The most mature concept-stage ML is target tractability: scoring how amenable a biological target is to therapeutic intervention. Framed as a learning task it is a supervised classification/ranking problem. The features are everything known about a target — genetic association with disease, expression patterns across tissues, the existence of a binding pocket, prior chemical or biological matter against it, safety signals from knockouts, network position in pathways. The label, learned from history, is whether targets like this one have yielded approved drugs. The model outputs a tractability score, sometimes split by modality ("small-molecule tractable" versus "antibody tractable" versus "intractable today").

Open, peer-reviewed resources have made this concrete. The Open Targets platform integrates genetic, genomic, and chemical evidence into target-disease association scores and an explicit tractability assessment, and it is the canonical public example of evidence-integration learning at the head of the pipeline [1]. The framing matters for us: tractability is mostly answering "is this a good idea biologically, and reachable by some modality?" It is not answering "is the molecule that reaches it manufacturable?" Those can sharply disagree. A target that is beautifully antibody-tractable might only be engageable by a tetravalent bispecific with a known propensity to aggregate — high biological tractability, low manufacturability. The tractability model, trained on approvals, will not warn you, because plenty of hard-to-make molecules still got approved; the manufacturing pain never made it into the label.

So target tractability earns its place in a manufacturing book only at one remove: it is the step where modality gets implicitly decided, and modality is the single largest lever on manufacturability. The discipline this chapter argues for is to make that implicit decision explicit — to carry a manufacturability expectation alongside the tractability score, rather than discover the consequences three years later at tech transfer.

Evidence

Target tractability ML (e.g., Open Targets evidence integration) is (production) as a discovery decision-support tool and rests on peer-reviewed, independent infrastructure [1]. Its relevance to manufacturing is indirect and, to our knowledge, not formally validated — there is no published model that takes a target and predicts a downstream manufacturing outcome. Treat the manufacturing inference in this section as a reasoned argument, not an established result.

Manufacturability-by-design: the mindset before the model

"Quality by Design" (QbD), which the data book and Book 3 both lean on, says you build quality into a process rather than testing it in at the end. Manufacturability-by-design pushes the same logic one step further upstream: build manufacturability into the molecule rather than engineering around it later. The mindset is older than the ML — protein engineers have long known to avoid free cysteines, deamidation hotspots, and glycosylation sequons in the wrong places — but machine learning is beginning to turn that craft knowledge into quantitative, sequence-level predictions you can compute before a gene is even synthesized.

The vocabulary is developability: the set of biophysical properties that determine whether a candidate will survive expression, purification, formulation, storage, and administration. At concept, the relevant developability signals you can compute or estimate from sequence (and, with structure prediction, from a model of the fold) include:

• Expressibility / titer potential — will CHO cells make enough of it? A molecule the production bioreactor can only push to a fraction of platform titer is a cost problem forever.
• Aggregation propensity — the tendency to form the high-molecular-weight species that SEC measures (recall BATCH-2026-001 carries SEC_HMW_pct = 1.287%); high intrinsic aggregation makes every purification and formulation step harder.
• Solubility and viscosity — decisive for high-concentration formulation, which is where a subcutaneous mAb lives or dies; a viscous molecule may be undeliverable in a prefilled syringe regardless of efficacy.
• Chemical and conformational stability — deamidation, oxidation, isomerization, and unfolding liabilities that surface as charge variants (CEX) and degradation over shelf life.
• Immunogenicity and sequence liabilities — humanness and predicted T-cell epitopes, which bear on both safety and the analytical burden downstream.

The peer-reviewed picture in 2024–2025 is genuinely encouraging for antibody developability specifically. In-silico tools predicting antibody developability from sequence — for example PROPERMAB, which predicts multiple developability metrics from sequence and structure features [2] — and large-data ensemble models for the hardest property, high-concentration viscosity [3], and interpretable ML that not only predicts but explains which residues drive viscosity so engineers can fix them [4], are all real and published. The Therapeutic Antibody Profiler and similar flag-based screens established the prior generation of this idea: compute a handful of biophysical descriptors and compare a candidate against the distribution of approved antibodies, flagging outliers.

Two honest caveats keep this from being a solved problem. First, almost all of this maturity is antibody-specific — the moment a program chooses a non-mAb modality, the predictive tooling thins out dramatically, exactly when manufacturability risk is highest. Second, most developability ML operates on candidate sequences (a Chapter 5 concern) rather than on the target/modality choice this chapter is about; the concept-stage version is more of a prior — "molecules of this class, against targets like this, tend to be hard/easy to make" — than a precise prediction. The signal at concept is real but coarse.

Evidence

Antibody developability prediction (PROPERMAB; ensemble viscosity models; interpretable viscosity ML) is (research) trending toward decision-support, peer-reviewed and largely independent or academic [2][3][4]. Reported accuracies are model-and-dataset specific and, like every concept-stage number in this book, should be read as evidence the approach works on curated data, not as a guarantee on your next molecule. No tier-1 evidence exists for concept-stage manufacturability prediction of the target/modality choice itself.

The handoff gap: the hole at the head of the spine

Here is the most important — and least flattering — fact in this chapter. The information generated at concept and discovery, the very information that would let downstream manufacturing models do their jobs, largely fails to travel down the spine. Discovery and manufacturing are different organizations, on different timelines, with different data systems, different vocabularies, and different incentives. The discovery group optimizes for binding and efficacy and hands off a sequence; the manufacturing group inherits that sequence and re-discovers, empirically and expensively, the developability properties that were often knowable — or even computed and then discarded — at concept.

This is recognized in the field as a genuine spine gap, not a niche complaint [5]. It is why so much of what this book describes downstream is, in effect, paying for a decision made upstream without manufacturing in the room. The OOS sibling BATCH-2026-004 in our running dataset fails on HCP_ng_per_mg = 128.0 against a spec ceiling of 100.0 — a host-cell-protein clearance problem that is a process failure, not a molecule failure. But many real OOS events and many "this molecule is just hard" verdicts trace back to a concept-stage choice that nobody flagged because the information never crossed the handoff.

What would close the gap is not a clever model so much as a discipline of carrying a structured record across the boundary — the same contextualization discipline Book 2 applies to a data point and Book 4 applies to a knowledge-graph node. A learned, manufacturability-aware target profile would travel with the program: the target and modality, the tractability evidence, every in-silico developability prediction with its uncertainty, and an explicit manufacturability expectation that downstream models can read, test against reality, and feed back. The technology to compute the pieces increasingly exists. The pipe to carry them — and the organizational will to act on a manufacturing signal that costs the discovery team time — mostly does not. That is the unglamorous frontier.

The first node of the spine, learned: target, mechanism, and modality feed a tractability model and a panel of weak-but-real in-silico developability signals; the dashed handoff gap is the documented hole through which manufacturability knowledge usually leaks away, and the manufacturability-aware target profile is the record that should — but rarely does — carry it across. Original diagram by the authors, created with AI assistance.

A worked sketch: assembling a concept-stage target profile

This chapter contributes no heavyweight new model — concept-stage manufacturability is too weakly grounded to ship a confident predictor, and saying so honestly is part of the lesson. What it can show is the assembly discipline: pulling the running example's identity together with the kind of concept-stage signals that should accompany it, so the record exists to be carried across the handoff. The light helper lives alongside the suite's shared examples/platform/ml/dataio.py loader (which the rest of Book 5 uses to read the committed datasets), and reads the genealogy and release data we already have to anchor the profile in real values rather than invented ones.

# examples/platform/ml/target_profile.py
# Concept-stage target profile for mAb-A. Honest by construction: the manufacturing
# OUTCOMES are real (read from the release dataset); the concept-stage PREDICTIONS are
# labelled illustrative, because no validated concept->manufacturing model exists yet.
from dataclasses import dataclass, field
import pandas as pd
from dataio import DATASETS  # shared loader used across examples/platform/ml/

@dataclass
class TargetProfile:
    program: str
    target: str
    mechanism: str
    modality: str
    tractability_score: float          # discovery-side, modality-aware (illustrative)
    developability: dict               # concept-stage in-silico signals (illustrative)
    realized_cqas: dict = field(default_factory=dict)  # what actually happened, downstream

def realized_cqas_for(batch_id: str) -> dict:
    """The downstream truth this concept choice eventually produced — REAL values."""
    df = pd.read_csv(DATASETS / "hplc_results.csv")
    b = df[df.batch_id == batch_id].set_index("test")
    return {
        "SEC_monomer_pct": float(b.loc["SEC_monomer_pct", "value"]),
        "SEC_HMW_pct":     float(b.loc["SEC_HMW_pct", "value"]),   # aggregation, realized
        "release":         "PASS" if (b["result"] == "PASS").all() else "OOS",
    }

profile = TargetProfile(
    program="mAb-A",
    target="<antigen, discovery-defined>",
    mechanism="antigen engagement (IgG1)",
    modality="monoclonal antibody, CHO",
    tractability_score=0.81,           # illustrative
    developability={                   # illustrative concept-stage predictions, each with uncertainty
        "expressibility":   {"score": 0.78, "ci": 0.15, "note": "platform IgG1, high titer prior"},
        "aggregation":      {"score": 0.12, "ci": 0.10, "note": "low predicted HMW propensity"},
        "viscosity_risk":   {"score": 0.20, "ci": 0.18, "note": "below high-conc concern threshold"},
    },
    realized_cqas=realized_cqas_for("BATCH-2026-001"),
)
print(profile)

TargetProfile(program='mAb-A', target='<antigen, discovery-defined>',
  mechanism='antigen engagement (IgG1)', modality='monoclonal antibody, CHO',
  tractability_score=0.81,
  developability={
    'expressibility': {'score': 0.78, 'ci': 0.15, 'note': 'platform IgG1, high titer prior'},
    'aggregation':    {'score': 0.12, 'ci': 0.10, 'note': 'low predicted HMW propensity'},
    'viscosity_risk': {'score': 0.20, 'ci': 0.18, 'note': 'below high-conc concern threshold'}},
  realized_cqas={'SEC_monomer_pct': 98.611, 'SEC_HMW_pct': 1.287, 'release': 'PASS'})

# The concept-stage prediction (low aggregation, score 0.12) is CONSISTENT with the
# realized SEC_HMW_pct of 1.287% (well inside the 0..3% spec) — but a single consistent
# example proves nothing. The point is structural: predictions and outcomes now live in
# ONE record that can be carried across the handoff and graded as real data accrues.

The numbers labelled illustrative are exactly that — placeholders standing in for the kind of signal a concept-stage developability tool would emit, with an uncertainty (ci) on each because, at this node, the uncertainty is the message. The numbers that are real — 98.611, 1.287, the PASS verdict — come straight from hplc_results.csv for the golden batch. The discipline the snippet embodies is the whole argument: put the weak early prediction and the eventual hard outcome in the same record, so the handoff carries something gradeable instead of nothing at all.

Anatomy of a manufacturability-aware target profile

Every chapter in this series dissects one record. Here the record is the artifact that should cross the handoff gap: a concept-stage target profile that fuses the discovery decision with a manufacturing expectation and, eventually, the realized outcome. It is the head-of-spine analogue of the soft-sensor prediction record and the batch node — the same idea that a useful artifact carries its provenance, its uncertainty, and the means of its own grading.

One target profile, unpacked: the discovery-side choice (target, mechanism, modality, tractability), the weak-but-explicit concept-stage developability predictions each with its uncertainty, the realized CQAs filled in years later from real release data, and the relationships that should carry it across the handoff gap and feed the grading loop back. The card's whole purpose is to make a concept-stage choice auditable against what it eventually produced. Original diagram by the authors, created with AI assistance.

Read the card the way a CMC lead would. The discovery block is the inherited decision — and crucially it is labelled as discovery-side, so no one mistakes a binding-driven choice for a manufacturing-vetted one. The manufacturability block is this chapter's contribution: every developability number carries a visible uncertainty band, because a concept-stage prediction with a hidden uncertainty is worse than no prediction — it invites false confidence. The realized-outcome block is the green core, and it is deliberately empty at concept and filled in later from real data (SEC_monomer_pct = 98.611, SEC_HMW_pct = 1.287, release = PASS), which is what turns the profile from a guess into a record that can be graded. The relationships panel is where the handoff lives: a forward edge across the dashed gap to molecule discovery, cell-line development, and the production bioreactor, and a feedback edge — grade-the-prediction — that closes the loop once outcomes accrue. The asymmetry in the footer is the same one that haunts every soft sensor in this book: the prediction is cheap and now, the truth is expensive and later.

The unsolved part: this is the weakest-grounded ML in the book

It would be dishonest to dress this chapter as a success story. Concept-stage manufacturability prediction is, by a wide margin, the least well-validated machine learning in Book 5, and the reasons are structural rather than fixable with more compute.

The first reason is the cold-start problem at its most extreme — the binding constraint Part I named for all of bioprocess ML, here in its purest form [6]. To learn "which concept-stage choices lead to manufacturable molecules," you need many programs that each went from concept all the way to commercial manufacturing with the concept-stage features recorded. A large company runs a few such programs per year, most candidates die before manufacturing for reasons unrelated to manufacturability, and the survivors take years to produce a label. The training set is not small; it is almost nonexistent, and it grows at the speed of drug development itself.

The second reason is survivorship bias baked into every label. The only molecules with a "manufacturable" label are the ones that got made — which means the model never sees the molecules that were killed for manufacturability reasons at concept, and never learns the failure boundary it most needs. A tractability model trained on approvals has the same blind spot: hard-to-make molecules that were approved anyway teach the model that difficulty does not matter.

The third reason is the handoff gap itself as a data problem. Even where a program does run end to end, the concept-stage features and the manufacturing outcomes live in different systems, were never linked, and often the concept-stage predictions were never persisted at all. You cannot train a model to bridge a gap whose two sides were never connected — which is why the assembly-discipline record above matters more, today, than any predictor.

The honest verdict, then, is the one Part I set up and the final chapter will return to: at the head of the spine, ML is a structured-prior generator, not an oracle. It can surface modality-level manufacturability concerns ("high-concentration viscosity risk for this format"), encode them with uncertainty, and carry them forward to be tested — and that is genuinely valuable. It cannot tell you, at concept, whether your specific molecule will hit 98.611% monomer or land in the HCP ditch that sank BATCH-2026-004. Anyone selling the second thing is selling the swamp.

What this chapter adds to the model suite

This chapter is deliberately light on new code, in proportion to how lightly grounded the science is. It contributes:

• examples/platform/ml/target_profile.py — the small, honest helper sketched above: a TargetProfile dataclass that fuses the discovery decision, the (illustrative) concept-stage developability predictions with explicit uncertainties, and the real realized CQAs read from hplc_results.csv. Its purpose is the assembly discipline, not prediction.
• A reliance on the suite's shared examples/platform/ml/dataio.py loader, which every later chapter uses to read the committed datasets, keeping the running example's IDs and values identical across the book.

No model is trained here on purpose. The contribution is a record schema and a stance: make the weak early signal explicit, attach its uncertainty, bind it to the outcome it will eventually be graded against, and build the pipe that carries it across the handoff. The heavyweight predictors begin in Chapter 5, where there is enough data — candidate sequences with measured developability — to actually learn.

Why it matters

The whole rest of Book 5 is, in a sense, the consequence of this node. Every soft sensor, every hybrid model, every computer-vision inspector downstream is working on a molecule and a modality chosen here. Get the first node right — pick a manufacturable modality, carry a manufacturability-aware profile across the handoff, listen to the weak concept-stage signal instead of ignoring it — and the downstream models have an easier job and the process has fewer fights. Get it wrong, or simply fail to carry the information, and the most sophisticated downstream ML in the world is reduced to mitigating a decision it was never allowed to influence. Manufacturability really is chosen at concept; the only question is whether anyone with manufacturing knowledge — human or model — was in the room when it was chosen. That is why a manufacturing book has to start here, even though the ML here is the thinnest in the book.

In the real world

The honest industry picture matches the honest scientific one. Target tractability decision-support (Open Targets and commercial equivalents) is routine in discovery, but as a biology tool [1] — its manufacturing relevance is rarely formalized. Antibody developability prediction is the brightest spot: peer-reviewed, increasingly accurate sequence-and-structure models for aggregation, viscosity, and related liabilities are real and being adopted, though they sit mostly at the candidate-sequence stage of Chapter 5 rather than the target/modality stage [2][3][4]. A few large companies have stood up "manufacturability index" data lakes that score and rank candidates against accumulated internal manufacturing history — the CLD 4.0 line of work points in this direction [7] — but these are first-party systems whose performance is self-reported, and they live closer to cell-line development than to concept.

On governance, this node sits before the GMP boundary, so it is not directly touched by the Annex 22 restrictions on AI in critical GMP tasks [8] or the FDA's model-credibility framework [9] — a concept-stage developability prediction is decision support, not a GMP record. But the framing already matters: if a concept-stage model's output ever propagates into a regulatory submission or a control strategy, it inherits the credibility expectations of wherever it lands. The discipline of attaching uncertainty and provenance to the target profile now is what makes that propagation defensible later. The realistic 2026 state, consistent with the broader survey reality that AI/ML has the most pilots and the fewest scaled deployments, is that concept-stage manufacturability ML is (research) edging toward (pilot) decision support — promising, peer-reviewed in its antibody-developability core, and nowhere near an oracle.

Key terms

• Target — the biological molecule a therapy is designed to engage; the first choice at the head of the manufacturing spine.
• Mechanism of action (MoA) — how the therapy engages the target (block, degrade, recruit, deliver).
• Modality — what kind of molecule embodies the mechanism (mAb, bispecific, ADC, fusion, cell/gene therapy); the single largest lever on manufacturability.
• Target tractability / druggability — an ML-scored estimate of how amenable a target is to therapeutic intervention; a biology signal that only indirectly bears on manufacturing via modality.
• Manufacturability-by-design — building manufacturability into the molecule at concept rather than engineering around it later; the QbD mindset pushed one step upstream.
• Developability — the biophysical properties (expressibility, aggregation, solubility, viscosity, stability, immunogenicity) that determine whether a candidate survives the spine.
• In-silico developability prediction — computing developability signals from sequence (and predicted structure) before a molecule is made; mature for antibodies, thin for other modalities.
• The handoff gap — the documented hole through which manufacturability knowledge generated at concept/discovery fails to reach manufacturing.
• Manufacturability-aware target profile — the proposed structured record that carries the target/modality decision, its concept-stage predictions with uncertainty, and the realized outcomes across the handoff to be graded.
• Cold start (at concept) — the extreme small-data regime where almost no programs have run concept-to-commercial with concept-stage features recorded, so concept-stage manufacturability ML is barely trainable.
• Survivorship bias (in labels) — the distortion from only ever labelling molecules that survived to manufacturing, hiding the failure boundary a model most needs to learn.

Where this leads

The target is chosen, the modality is fixed, and — at best — a manufacturability-aware profile is ready to travel down the spine. The next chapter, Molecule Discovery: Generative Design and Developability Prediction, steps from the target/modality choice to the choice among candidate sequences, where there is finally enough data to train real models: the generative-design loop that proposes molecules and the developability predictors that score them, run together so that what gets advanced is not just the best binder but the best makeable binder. It is where the weak concept-stage prior of this chapter becomes a quantitative selection pressure.