AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Leveraging Local Perturbations to Map Allosteric Networks of Phosphatases

Allostery is central to regulation of protein function, but our mechanistic understanding remains incomplete. A deeper understanding of how redistributions of conformational states drive allostery in proteins could allow us to better grasp natural regulatory principles in cells and open new doors to therapeutic development. Conformationally dynamic human Protein Tyrosine Phosphatases (PTPs) exemplify the challenges and opportunities associated with allostery. The archetypal PTP, PTP1B, has been highly validated as a therapeutic target but no allosteric inhibitors have been approved for clinical use. This is largely because our understanding of the mechanisms underlying allostery in PTP1B remains limited, despite the discovery of putative allosteric sites in PTP1B. In the work described in this dissertation, I used high-throughput small-molecule fragment soaking and room-temperature X-ray crystallography to determine how temperature affects the occupancy, pose, and location of small-molecule fragments binding to PTP1B. These structures also indicated that temperature can modulate protein conformational responses to ligand binding, leading to new insights into allosteric networks. Building on fragment-bound structures, I worked to apply structure based drug design (SBDD) methods to design allosteric molecules that can modulate PTP1B function. In particular, I designed a new approach to bias the conformational ensemble of the targeted allosteric site using multistate docking simulations. I also worked with biotech companies who applied their technologies to a SBDD approach to design potential allosteric modulators. We uncovered both allosteric inhibitors and benign binders, and I determined the crystal structure of a designed compound bound to the targeted allosteric site in PTP1B. Lastly, I used computational and molecular modeling to map clinically relevant PTP mutations onto the structures of PTP1B and a close homolog, T-Cell Protein Tyrosine Phosphatase (TCPTP), and uncovered that the mutations likely enact their functional effects by perturbing allosteric sites and allosteric networks of the protein. Overall, this research, which marries unique techniques in experimental X-ray crystallography and computational structural biology, provides unique insights into protein-ligand interactions, particularly for allosteric sites, and provides promising new allosteric ligand footholds for a biomedically important enzyme. More broadly, this work will improve our general understanding of how transitions between multiple conformational states underlie allosteric regulation