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<!DOCTYPE html>
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<title>QuantisMol — AI-Powered ADMET Prediction for Drug Discovery</title>
<meta name="description" content="AI-powered ADMET prediction and compound prioritization tools to help drug discovery teams de-risk earlier.">
<meta property="og:title" content="QuantisMol — AI-Powered ADMET Intelligence">
<meta property="og:description" content="AI-powered computational tools for ADMET prediction, molecular property assessment, and translational decision support in drug discovery.">
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</style>
</head>
<body>
<nav>
<div class="container">
<a href="#" class="logo">QuantisMol</a>
<ul>
<li><a href="#platform">Platform</a></li>
<li><a href="#research">Research</a></li>
<li><a href="#team">Team</a></li>
<li><a href="#contact">Contact</a></li>
</ul>
</div>
</nav>
<section class="hero">
<div class="container">
<div class="hero-eyebrow fade-up">Computational Drug Discovery</div>
<h1 class="fade-up delay-1">AI-driven ADMET intelligence for smarter drug discovery</h1>
<p class="hero-sub fade-up delay-2">
QuantisMol builds AI-powered tools that help drug discovery teams predict molecular behavior, flag development risks, and prioritize compounds with greater confidence.
</p>
<a href="#platform" class="hero-cta fade-up delay-3">
Explore the platform
<svg viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="1.5"><path d="M3 8h10M9 4l4 4-4 4"/></svg>
</a>
</div>
</section>
<section class="workflow fade-up delay-4">
<div class="container-wide">
<div class="workflow-label">How it works</div>
<div class="workflow-img-wrap">
<img src="quantismol-admet-workflow.png" alt="QuantisMol ADMET workflow — from molecular space through AI engine to absorption, distribution, metabolism, excretion, and toxicity predictions">
</div>
<p class="workflow-caption">From chemical space exploration through AI-driven prediction across all five ADMET dimensions to actionable development insights.</p>
</div>
</section>
<section class="pillars" id="platform">
<div class="container">
<div class="section-label fade-up">Core capabilities</div>
<div class="pillar-grid">
<div class="pillar fade-up delay-1">
<div class="pillar-num">01</div>
<h3>ADMET Prediction</h3>
<p>Predict absorption, distribution, metabolism, excretion, and toxicity profiles for candidate compounds using models trained on curated pharmacokinetic datasets.</p>
<span class="pillar-tag tag-green">Predictive Models</span>
</div>
<div class="pillar fade-up delay-2">
<div class="pillar-num">02</div>
<h3>Compound Prioritization</h3>
<p>Rank and filter molecular libraries against multi-objective criteria — balancing potency, selectivity, and developability to surface the most promising leads.</p>
<span class="pillar-tag tag-copper">Decision Support</span>
</div>
<div class="pillar fade-up delay-3">
<div class="pillar-num">03</div>
<h3>Translational Risk Analysis</h3>
<p>Bridge computational predictions with experimental strategy. Identify liabilities that matter for IND-enabling studies before committing to costly in vivo work.</p>
<span class="pillar-tag tag-blue">Translational Science</span>
</div>
</div>
</div>
</section>
<section class="research" id="research">
<div class="container">
<div class="section-label fade-up">Research</div>
<div class="research-layout">
<div class="research-text fade-up delay-1">
<h2>Open, reproducible, benchmark-driven</h2>
<p>Our research program prioritizes practical utility over novelty. Every model is evaluated against real-world drug discovery scenarios, not just leaderboard metrics.</p>
<p>Preprints, datasets, and open-source tools will be released as the platform matures.</p>
<!-- <a href="https://github.com/quantismol" class="research-link">
View on GitHub
<svg width="14" height="14" viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="1.5"><path d="M3 8h10M9 4l4 4-4 4"/></svg>
</a> -->
</div>
<div class="fade-up delay-2">
<ul class="research-principles">
<li>
<span class="principle-num">01</span>
<span class="principle-text">Benchmark-first model evaluation</span>
</li>
<li>
<span class="principle-num">02</span>
<span class="principle-text">Reproducible training and inference pipelines</span>
</li>
<li>
<span class="principle-num">03</span>
<span class="principle-text">Calibrated uncertainty estimates</span>
</li>
<li>
<span class="principle-num">04</span>
<span class="principle-text">Validated on real program data</span>
</li>
</ul>
</div>
</div>
</div>
</section>
<section class="founder" id="team">
<div class="container">
<div class="section-label fade-up">Team</div>
<div class="founder-layout">
<!-- <div class="founder-avatar fade-up delay-1">JY</div> -->
<div class="fade-up delay-2">
<h2 class="founder-info">About QuantisMol</h2>
<!-- <div class="founder-title">Founder, QuantisMol</div> -->
<p class="founder-bio">QuantisMol was founded by scientists and computational researchers with experience across drug discovery, DMPK, bioanalysis, translational sciences, computer science, and machine learning. The company was created to address a recurring challenge in pharmaceutical research: identifying ADMET liabilities early enough to meaningfully influence decision-making.</p>
<p class="founder-bio">By combining modern machine learning approaches with practical drug development expertise, QuantisMol aims to help research teams evaluate compounds more efficiently, prioritize better candidates, and reduce late-stage attrition.</p>
</div>
</div>
</div>
</section>
<!-- <section class="publications">
<div class="container">
<div class="section-label fade-up">Selected publications</div>
<ul class="pub-list fade-up delay-1">
<li>
<span class="pub-title">In vitro structure-activity relationship stability study of antisense oligonucleotide therapeutics using biological matrices and nucleases.</span> Lovrić, J.; <strong>Yan, J.</strong>; Li, X.-Q.; et al. <span class="pub-journal">Pharmacology Research & Perspectives</span>, 2025, 13, e70096.
</li>
<li>
<span class="pub-title">Application of accelerator mass spectrometry to characterize the mass balance recovery and disposition of AZD4831.</span> Bhattacharya, C.; Sandinge, A.-S.; Bragg, R. A.; <strong>Yan, J.</strong>; et al. <span class="pub-journal">Drug Metabolism & Disposition</span>, 2023, 51, 451.
</li>
<li>
<span class="pub-title">MOF decomposition and introduction of repairable defects using a photodegradable strut.</span> <strong>Yan, J.</strong>; MacDonald, J.; Burdette, S. <span class="pub-journal">Chemistry — A European Journal</span>, 2019, 25, 8393.
</li>
<li>
<span class="pub-title">Detection of adsorbates on emissive MOF surfaces with X-ray photoelectron spectroscopy.</span> <strong>Yan, J.</strong>; Carl, A. D.; Maag, A. R.; et al. <span class="pub-journal">Dalton Transactions</span>, 2019, 48, 4520.
</li>
</ul>
</div>
</section> -->
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<div class="section-label fade-up">Get in touch</div>
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<h2>Start a Conversation</h2>
<p>Whether you're exploring ADMET tools for your pipeline, interested in early access, or looking to collaborate on research — reach out.</p>
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<div class="contact-links fade-up delay-2">
<a href="mailto:info@quantismol.com" class="contact-link">
<span class="link-label">Email</span>
info@quantismol.com
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<span class="link-label">Location</span>
Orange County, California
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<p>© 2026 QuantisMol LLC. All rights reserved.</p>
<p>Orange County, CA</p>
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