adlvdl.github.io/services.html at master · adlvdl/adlvdl.github.io · GitHub

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<!DOCTYPE html>
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  <title>Services — Antonio de la Vega de León</title>
  <meta name="description" content="Design and implementation of ML workflows for drug discovery as well as teaching ML and chemoinformatics.">
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    <a class="nav-logo" href="index.html">delavega.ai</a>
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      <li><a href="index.html">Home</a></li>
      <li><a href="services.html" class="nav-active">Services</a></li>
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      <li><a href="about.html">About</a></li>
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        <span class="mono-tag">consulting</span>
        <h1>Services</h1>
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      <p style="font-size:0.95rem; color:var(--text-2); max-width:58ch; margin-bottom:0.5rem;">
        I work with pharmaceutical and biotech teams to design machine learning workflows for drug discovery.
        Whether you need a one-off analysis, a modelling pipeline, or ongoing scientific advisory, I can help.
      </p>

      <div class="service-list" style="margin-top:2rem;">

        <div class="service-card">
          <span class="mono-tag">machine learning</span>
          <h3>Molecular Property Prediction</h3>
          <p>
            Building machine learning models to predict biological activity,
            toxicity, or ADME properties from molecular structure. Designing workflows
            to track models trained, their expected performance and impact on key
            impact metrics for drug discovery projects
          </p>
        </div>

        <div class="service-card">
          <span class="mono-tag">explainability</span>
          <h3>ML Model Interpretation &amp; Explainability</h3>
          <p>
            Making black-box models understandable — identifying which structural features drive predictions,
            flagging unreliable predictions, and generating actionable insights for medicinal chemistry teams.
          </p>
        </div>

        <div class="service-card">
          <span class="mono-tag">generative ai</span>
          <h3>De-novo Drug Design</h3>
          <p>
            Implement computational chemical ideation engines, using traditional
            chemoinformatic techniques and generative models, to provide high quality
            molecular candidates for synthesis and evaluation.
          </p>
        </div>

        <div class="service-card">
          <span class="mono-tag">chemoinformatics</span>
          <h3>Chemoinformatics Pipeline Development</h3>
          <p>
            Designing and implementing end-to-end chemoinformatics workflows: molecular fingerprint generation,
            chemical space analysis, virtual screening pipelines, and matched molecular pair analysis.
          </p>
        </div>

        <div class="service-card">
          <span class="mono-tag">advisory</span>
          <h3>Scientific Consulting &amp; Advisory</h3>
          <p>
            Strategic guidance on ML and cheminformatics approaches for your drug discovery programme.
            Includes literature review, method selection, and review of existing models or analyses.
          </p>
        </div>

        <div class="service-card">
          <span class="mono-tag">education</span>
          <h3>Teaching ML and Cheminformatics</h3>
          <p>
            Workshops and training sessions for research teams — from introductory machine learning for
            chemists to advanced topics in interpretable AI and foundational models for drug design.
          </p>
        </div>

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          <h3>Let's work together</h3>
          <p>Book a free 30-minute intro call to discuss your project.</p>
        </div>
        <a href="https://calendar.proton.me/bookings#srNPliRTvP3FDewAuttYXfh4r6iIdNgXm46-TZyGOKA=" class="btn-primary" target="_blank" rel="noopener">Book a call →</a>
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    <span class="footer-logo">delavega.ai · 2026</span>
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