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

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  <title>Research — Antonio de la Vega de León</title>
  <meta name="description" content="Research interests and history: machine learning interpretation, chemical space visualisation, cheminformatics.">
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        <span class="mono-tag">← <a href="about.html">about</a></span>
        <h1>Research</h1>
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      <!-- Current interests -->
      <div class="about-section">
        <h2 class="mono-tag">current interests</h2>
        <p>
          My research interests centre on the use of machine learning and visualisation in pharmaceutical
          research to aid decision making. I am particularly interested in:
        </p>
        <ul>
          <li>
            <strong>Machine learning model interpretation</strong> — ML models learn associations between
            structural elements of molecules and biological properties that are expensive to measure.
            I develop techniques that make these models understandable, so structural insights can
            inform compound design.
          </li>
          <li>
            <strong>Visualisation of chemical space</strong> — developing novel visualisations that bridge
            the structural information of sets of molecules to their physico-chemical and biochemical
            properties, condensing large amounts of information into interpretable views.
          </li>
        </ul>
      </div>

      <!-- Generative AI -->
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        <h2 class="mono-tag">learned representations</h2>
        <p>
          Generative models trained on large corpora of molecular structures generate internal
          representations of molecules that can be of use in property prediction. Our work showed that
          without finetuning, that internal representation of models pretrained on 2 billion structures were no better than
          traditional chemical fingerprints.


        </p>
        <img src="images/research/learned_representation.png" alt="Missing data analysis illustration" style="margin-top:1.25rem; max-width:100%; border-radius:4px; background-color:#ffffff; padding:0.5rem;">
      </div>

      <!-- D3i4AD -->
      <div class="about-section">
        <h2 class="mono-tag">D3i4AD project</h2>
        <p>
          I worked as a Marie Curie fellow as part of the European project
          Diagnostics
          and Drug Discovery Initiative for Alzheimer's Disease (D3i4AD). I developed deep neural
          networks with a dual purpose: predicting whether a compound would be active in a phenotypic
          screen, and identifying which target in the studied pathway it acted against. These models
          were used to screen compound libraries and to aid in the target deconvolution of hit compounds.
        </p>
      </div>

      <!-- Machine learning -->
      <div class="about-section">
        <h2 class="mono-tag">machine learning</h2>
        <p>
          Machine learning generates mathematical models that can predict a range of properties given
          suitable training data. I first worked on using matched molecular pairs as input to predict
          changes in activity caused by small chemical modifications. I also analysed how missing data
          affects the performance of multitask machine learning, providing an approximation of how much
          performance can be gained by testing new compound–assay combinations.
        </p>
        <img src="images/research/missing_data.png" alt="Missing data analysis illustration" style="margin-top:1.25rem; max-width:100%; border-radius:4px; background-color:#ffffff; padding:0.5rem;">
      </div>

      <!-- Visualisation -->
      <div class="about-section">
        <h2 class="mono-tag">visualisation of chemical space</h2>
        <p>
          Chemical space is the collection of all physically possible compounds. Public and private
          collections have become very large, making systematic structure–activity analysis complicated.
          Tailored visualisations ease this analysis by providing general or focused views of chemical
          space.
        </p>
        <p style="margin-top:0.75rem;">
          A large part of my thesis was dedicated to the design and development of visualisations for
          complex, high-dimensional chemical spaces. I expanded the chemical space network concept to
          generate coordinate-free representations of coordinate-based chemical space, and introduced
          a layout algorithm to provide global-view character to these networks. I also developed
          multi-objective chemical space views and collaborated with Pfizer scientists to assess the
          progression of lead optimisation series using SAR matrices and visualisations.
        </p>
        <img src="images/research/visualizations.png" alt="Chemical space visualisation" style="margin-top:1.25rem; max-width:100%; border-radius:4px; background-color:#ffffff; padding:0.5rem;">
      </div>

      <!-- Matched molecular pairs -->
      <div class="about-section">
        <h2 class="mono-tag">matched molecular pairs</h2>
        <p>
          Matched molecular pairs (MMPs) are compound pairs that share a large common substructure but
          differ at a specific site. They constitute an intuitive way to represent chemical similarity
          and have become increasingly important for molecular design. I performed several data mining
          analyses of MMPs on public databases (PubChem, ChEMBL), focusing on how structural
          modifications encoded in these pairs change physico-chemical properties like activity or
          ionizability. I also proposed an extension of the MMP concept using a retrosynthetic approach
          during pair generation.
        </p>
        <img src="images/research/mmps.png" alt="Matched molecular pairs illustration" style="margin-top:1.25rem; max-width:100%; border-radius:4px;">
      </div>

      <!-- Molecular modelling -->
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        <h2 class="mono-tag">molecular modelling</h2>
        <p>
          Docking of compounds onto protein binding pockets provides hypotheses about binding
          conformation that can inform further optimisation. I assisted medicinal chemistry groups
          by performing docking studies on compound series, and worked on conformational analysis
          of macrocycles — complex molecules possessing a large ring, many of which are interesting
          natural products. We compared traditional conformational sampling (LowModeMD) against short
          MD simulations to determine which better identified active conformations.
        </p>
        <img src="images/research/modeling.png" alt="Molecular modelling illustration" style="margin-top:1.25rem; max-width:100%; border-radius:4px;">
      </div>

      <!-- Bioinformatics -->
      <div class="about-section">
        <h2 class="mono-tag">bioinformatics</h2>
        <p>
          During the final year of my undergraduate degree I was part of Prof. Julian Perera's
          workgroup. Using partial sequencing results for <em>Rhodococcus ruber</em> strain Chol-4,
          I performed the first partial assembly of the contigs and characterised the genomic regions
          containing the genes responsible for cholesterol degradation.
        </p>
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