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         <div class="prose prose-slate prose-lg max-w-none mb-24 leading-relaxed text-slate-700 border-b pb-20">
             <h2 class="text-3xl font-bold text-slate-900 mb-6">Introduction</h2>
             <p>
-                The challenge of modern AI in drug discovery is often reduced to a static prediction task: "Does this molecule bind to this target?" But in a real-world biotech lab, the answer is never that simple. A lead chemist must balance <strong>potency</strong> against <strong>toxicity</strong>, <strong>synthesizability</strong>, and a rapidly depleting <strong>assay budget</strong>.
+                In traditional drug discovery tasks, LLMs are often asked to "generate a molecule" in a single shot. But science doesn't happen in a vacuum. It happens in the loop—through trial, error, and verification. 
             </p>
             <p class="mt-4">
-                <strong>MolForge</strong> is a verifier-driven reinforcement learning environment that replicates this complexity. We don't trust the model to "guess" the answer. Instead, we force it to execute a sequence of actions—edits, assays, and specialist reviews—until it can justify a final submission with verifiable evidence.
+                <strong>MolForge</strong> is a reinforcement learning environment that simulates a medical oncology discovery lab. It forces the model to navigate real-world constraints: limited budget, molecular toxicity, and synthesis complexity.
             </p>
             
             <div class="mt-12 p-8 bg-slate-900 text-slate-200 rounded-2xl shadow-lg">
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             </div>
         </div>
 
-        <!-- Scientific Architecture -->
+        <!-- The Scientific Verifier Stack -->
+        <section class="mb-32">
+            <div class="flex items-center gap-3 mb-8">
+                <div class="w-10 h-10 rounded-lg bg-emerald-600 flex items-center justify-center text-white font-bold">🧪</div>
+                <h2 class="text-3xl font-bold tracking-tight">The Scientific Verifier Stack</h2>
+            </div>
+            
+            <p class="text-slate-600 mb-10 text-lg leading-relaxed">
+                MolForge doesn't just predict outcomes; it utilizes multiple simulation layers to ground the model's decisions in chemical and biological reality.
+            </p>
+
+            <div class="grid md:grid-cols-3 gap-6 mb-12">
+                <div class="shadcn-card p-8 bg-white hover:border-emerald-500 transition-all cursor-default">
+                    <div class="w-12 h-12 bg-emerald-50 text-emerald-600 rounded-xl flex items-center justify-center mb-6 text-xl">🧬</div>
+                    <h4 class="font-bold text-lg mb-3">RDKit</h4>
+                    <p class="text-sm text-slate-500 leading-relaxed italic">"Keeping molecules physically possible"</p>
+                    <p class="text-sm text-slate-600 mt-4 leading-relaxed">
+                        RDKit acts as the fundamental chemistry ruleset. It checks for molecular valency, ensures every edit is chemically plausible, and calculates core descriptors like Lipophilicity and TPSA.
+                    </p>
+                </div>
+                <div class="shadcn-card p-8 bg-white hover:border-blue-500 transition-all cursor-default">
+                    <div class="w-12 h-12 bg-blue-50 text-blue-600 rounded-xl flex items-center justify-center mb-6 text-xl">💊</div>
+                    <h4 class="font-bold text-lg mb-3">TDC Oracles</h4>
+                    <p class="text-sm text-slate-500 leading-relaxed italic">"Predicting biomedical fate"</p>
+                    <p class="text-sm text-slate-600 mt-4 leading-relaxed">
+                        Utilizing the Therapeutics Data Commons, MolForge predicts real-world ADMET properties, toxicity risks, and synthesizability scores (SA_Score) for every candidate.
+                    </p>
+                </div>
+                <div class="shadcn-card p-8 bg-white hover:border-indigo-500 transition-all cursor-default">
+                    <div class="w-12 h-12 bg-indigo-50 text-indigo-600 rounded-xl flex items-center justify-center mb-6 text-xl">🎯</div>
+                    <h4 class="font-bold text-lg mb-3">Heuristic Docking</h4>
+                    <p class="text-sm text-slate-500 leading-relaxed italic">"Simulating receptor-drug fit"</p>
+                    <p class="text-sm text-slate-600 mt-4 leading-relaxed">
+                        A fast, physics-inspired simulation that updates potency in milliseconds based on structural pocket matching and receptor complementarity.
+                    </p>
+                </div>
+            </div>
+
+            <div class="p-8 bg-slate-50 border border-slate-200 rounded-2xl">
+                <h5 class="font-bold text-slate-900 mb-6 flex items-center gap-2">
+                    <span class="w-2 h-2 bg-indigo-500 rounded-full"></span>
+                    The 3 Rules of Potency Simulation
+                </h5>
+                <div class="grid md:grid-cols-3 gap-8">
+                    <div class="space-y-2">
+                        <p class="font-bold text-sm text-slate-800">1. Pocket Matching</p>
+                        <p class="text-xs text-slate-500 leading-relaxed">Structural fit of the fragment (e.g., azaindole) into the KRAS G12C target pocket.</p>
+                    </div>
+                    <div class="space-y-2">
+                        <p class="font-bold text-sm text-slate-800">2. Lipophilic Match</p>
+                        <p class="text-xs text-slate-500 leading-relaxed">Targeting the ideal LogP of <strong>3.0</strong> for optimal binding without repulsive clashes.</p>
+                    </div>
+                    <div class="space-y-2">
+                        <p class="font-bold text-sm text-slate-800">3. Polarity Match</p>
+                        <p class="text-xs text-slate-500 leading-relaxed">Optimizing TPSA toward the ideal <strong>85.0</strong> to avoid polar clashes in hydrophobic pockets.</p>
+                    </div>
+                </div>
+            </div>
+        </section>
+
+        <!-- The POMDP Architecture -->
         <section class="mb-32">
             <div class="flex items-center gap-3 mb-8">
                 <div class="w-10 h-10 rounded-lg bg-indigo-600 flex items-center justify-center text-white font-bold">1</div>
@@ -127,7 +187,7 @@
                 MolForge is built as a <strong>Partially Observable Markov Decision Process (POMDP)</strong>. This means the agent never sees the "hidden truth" of the receptor. It only sees what its budget allows it to assay.
             </p>
 
-            <div class="shadcn-card p-4 bg-slate-50 mb-12">
+            <div class="shadcn-card p-4 bg-slate-50 mb-12 border-dashed">
                 <img src="assets/molforge_architecture.png" alt="Architecture" class="rounded-lg w-full">
                 <p class="mt-4 text-center text-xs text-slate-400 font-medium tracking-wide">THE SCIENTIFIC FEEDBACK LOOP: VERIFIER-FIRST DESIGN</p>
             </div>
@@ -230,24 +290,21 @@
 
             <div class="grid md:grid-cols-3 gap-6 mb-12">
                 <div class="shadcn-card p-6 bg-slate-50 border-t-4 border-t-indigo-500">
-                    <h4 class="font-bold mb-2">Coarse Shaping</h4>
+                    <h4 class="font-bold mb-2 text-sm uppercase tracking-wider text-slate-500">Coarse Shaping</h4>
                     <p class="text-xs text-slate-500">Edit feedback avoids exact hidden deltas, forcing the model to rely on empirical assays.</p>
                 </div>
                 <div class="shadcn-card p-6 bg-slate-50 border-t-4 border-t-emerald-500">
-                    <h4 class="font-bold mb-2">Evidence Multipliers</h4>
+                    <h4 class="font-bold mb-2 text-sm uppercase tracking-wider text-slate-500">Evidence Multipliers</h4>
                     <p class="text-xs text-slate-500">Submissions without current potency, toxicity, and synthesis support receive massive penalties.</p>
                 </div>
                 <div class="shadcn-card p-6 bg-slate-50 border-t-4 border-t-orange-500">
-                    <h4 class="font-bold mb-2">Budget Efficiency</h4>
+                    <h4 class="font-bold mb-2 text-sm uppercase tracking-wider text-slate-500">Budget Efficiency</h4>
                     <p class="text-xs text-slate-500">Small credits for valid evidence-backed submissions that use less than the allocated budget.</p>
                 </div>
             </div>
 
             <div class="p-6 bg-indigo-50 border border-indigo-100 rounded-xl text-sm">
-                <p class="font-bold text-indigo-700 mb-2">The Curriculum Mode Advantage</p>
-                <p class="text-indigo-900 leading-relaxed">
-                    For early RL, we add <strong>"Partial Credit Breadcrumbs"</strong>. If a model fails to submit but showed good scientific behavior (gathering evidence, designing promising molecules), it receives bounded warmup rewards. This solves the sparse reward problem and teaches the model how to explore before it discovers the terminal submission bonus.
-                </p>
+                <p class="font-bold text-indigo-700 mb-2 italic">"Curriculum mode is the RL warm-up engine—providing the breadcrumbs needed for the model to discover the submission bonus."</p>
             </div>
         </section>
 
@@ -306,7 +363,7 @@
         <!-- Final Takeaway -->
         <section class="mb-32 pt-20 border-t text-center">
             <h2 class="text-4xl font-black mb-6 tracking-tight">Final Takeaway</h2>
-            <p class="text-slate-500 max-w-2xl mx-auto mb-12 text-lg leading-relaxed">
+            <p class="text-slate-500 max-w-2xl mx-auto mb-12 text-lg leading-relaxed text-justify">
                 MolForge proves that scientific AI should not be built as a single-shot generator. By grounding the LLM in a <strong>closed-loop scientific environment</strong>, we can train models that respect budget, coordinate with specialists, and base their discoveries on verifiable evidence.
             </p>
             <div class="flex flex-wrap justify-center gap-4">