Research

AI-Driven De novo Drug Discovery

Computational drug design has greatly advanced the discovery of novel small molecules as advanced therapeutic agents, marking major milestones in the pharmaceuticalindustry. Techniques like molecular modeling, docking, and quantum mechanics remain essential in modern drug discovery. Recently, integrating artificial intelligence (AI) with these methods has accelerated the identification of potential drug candidates. AI algorithms can efficiently analyze large biological and chemical datasets, predict drug-target interactions, and optimize lead compounds, significantly reducing the time, cost, and resources needed for development. This AI-computational synergy has notably advanced the De Novo Drug Discovery process. As of December 2023, 24 AI-discovered molecules had completed Phase I trials, with an 80–90% success rate—demonstrating AI's growing impact on pharmaceutical research. In our current work, we are using AI-enhanced computational platforms to design small molecule anticancer drugs, some of which are advancing through preclinical and early clinical trials The integration of AI into computational pipelines marks a transformative step, offering unprecedented precision and efficiency in novel drug discovery.

Molecular Dynamics Simulation 

Our group's research leverages molecular dynamics (MD) simulations to explore critical drug discovery and molecular self-assembly phenomena. In drug discovery, MD simulations provide insights into ligand-receptor interactions, binding free energies, and conformational dynamics, enabling the rational design of potent therapeutic agents. We also investigate the aggregation behavior of small organic molecules in aqueous environments to understand the molecular mechanisms underlying nanoparticle formation, solubility, and stability. This dual focus aims to bridge the gap between fundamental molecular behavior and practical applications in drug development and nanomedicine, offering a computational lens to decode complex molecular processes.

Molecular modeling in drug discovery:

Molecular modeling is one of the techniques that is proving to be a game-changer in addressing the obstacles faced by drug discovery research. Although molecular modeling is a broad field, molecular docking, MD simulation, and ADMET modeling represent the three most widely used components of computational modeling and have been crucial in the identification of lead compounds for experimental in vitro and in vivo testing.

Currently, we are using these techniques for the i) design of small fluorescent biomolecules as the probes for the detection of biomarkers, ii) design and discovery of anticancer lead compounds, and iii) drug-repurposing. 

Fluorescent probes for bio-analyte detection:

 The development of small molecules as fluorescent probes has become increasingly important for the detection and imaging of bio-analytes such as biomarker proteins, cysteine, metal cations, and various anions.  The small molecular fluorescent probes are simple, show high selectivity, and sensitivity, whilst being non-invasive, and are suitable for real-time analysis of living systems. With this perspective, we work on the sensing mechanisms including Förster resonance energy transfer (FRET), intramolecular charge transfer (ICT), photoinduced electron transfer (PeT), excited-state intramolecular proton transfer (ESIPT), aggregation-induced emission (AIE) and multiple modality fluorescence approaches including dual/triple sensing mechanisms (DSM or TSM). We strive to find solutions to the various challenges in the development of small-molecule fluorescent probes suitable for biosensing and live-cell imaging applications.

Supramolecular architectures for molecular recognition:

Mimicry of the molecular recognition features of naturally occurring proteins by synthetic receptors is one of the challenging research topics of supramolecular chemistry. Biological receptors consist of large linear molecules that form 3D structures by specific intermolecular interactions. The recognition site offers precise stereochemistry and exhibits very efficient recognition processes by means of specific functional groups that constitute the entrance and inner surface of the cavity. The presence of specific functional groups at the mouth of the cavity suggests their role in the accessibility of substrates into the cavity. 

The pH of a solution shows a significant effect on the dynamics of the gate (formed by eight benzylic functions) and the portal on the hydrophobic cavity of the receptor. At pH 5.8 the gate closes and prohibits the entry of anionic guests. However, at pH 7.3 the gate opens and allows the entry of anionic guests into the hydrophobic cavity. It is the first time that an anionic receptor efficiently recognizes anionic guests.

 Biotechnology / Nanomaterial’s as therapeutics:

The functionalized calixarene derivatives exhibit remarkable properties towards organic and bioorganic molecules. However, the ability of calixarene derivatives to form stable complexes with biomolecules allows them to be applied to the development of biosensors in the field of biology, biotechnology, and drug discovery.