Drug Discovery Process Optimization

ChatGPT-5: AI + Physics–Driven Drug Discovery: Phase 1 — Target Identification & Validation Multi-Modal Target Prioritization: AI platforms such as TID-Pro (PandaOmics) integrate diverse omics, disease biology, and literature mining. Phase 2 — Structural & Dynamic Characterization Once promising targets are selected, accurate, state-rich molecular models are needed. High-Accuracy Static Structures: Cutting-edge predictors (AlphaFold 3, RosettaFold-3) produce atomic models for proteins and complexes with greater accuracy than older specialized tools. Flexibility & Conformational Sampling: Because druggability often depends on motion and hidden pockets; not static views: RMSF-net predicts residue flexibility directly from cryo-EM/PDB inputs. Distance-AF refines AlphaFold-type models into multiple functional states (active/inactive, ligand-bound). PROSE accelerates molecular dynamics by learning to sample the Boltzmann landscape efficiently, exploring metastable states with less compute. Experimental Feedback: High-resolution native top-down mass spectrometry (e.g., analyzed by precisION). Interface Mapping: Geometric deep learning (e.g., PeSTo) detects and classifies protein–protein, nucleic acid, ligand, or lipid interfaces, for allosteric sites and cryptic pockets. Phase 3 — Novel Ligand Design & Optimization With structural ensembles defined, AI generators propose molecules or macromolecular binders. Generative Small Molecules: TransPharmer creates new scaffolds guided by pharmacophore features to maintain essential activity (“scaffold hopping”). Graph neural networks enable property-driven inverse design, optimizing electronic or physicochemical traits. Large Molecule / Protein Binder Design: RFD2-MI (RoseTTAFold Diffusion 2) engineers binders for challenging epitopes, including PTM-modified peptides. La-Proteina builds fully atomistic proteins around chosen functional motifs for highly specific binding. Phase 4 — Scoring & Refinement Candidate ligands are triaged with both AI and physics to ensure physical plausibility and potency. Interaction-Aware Docking: Interformer models hydrogen bonds, hydrophobic effects, and other noncovalent forces while learning to penalize unrealistic poses. Full-Atom Refinement: FlowPacker repacks side chains using torsional flow models, giving chemically realistic, ready-to-synthesize complexes. Pipeline Impact Linking these elements creates an end-to-end, closed-loop system: Better early decisions reduce wasted effort. Dynamic, state-aware structures make pocket discovery and binder design more reliable. Generative chemistry and protein design yield diverse, property-optimized leads. Physics and pose-sensitive AI scoring focus resources on the most promising candidates. ...structural/dynamic modeling, generative design, and rigorous physical validation creates a highly automated, learning pipeline that accelerates drug discovery and de-risks costly late-stage failures."

Text understanding with #LLMs is useful but not enough for scientific understanding and discovery. In chemistry, in addition to text, chemical structure is essential to determine the properties of molecules. We have created the first multimodal text-chemical structure model: MoleculeSTM. It has an aligned latent space of both modalities. This allows the users to provide free-form text instructions to create molecules with arbitrary sets of properties. This enables zero-shot text-guided molecule editing (lead optimization) without the need to fine-tune the model for each new specification. Paper: bit.ly/4736BPH Code: bit.ly/4877YOS The core idea of MoleculeSTM is to align the chemical structure and textual description modalities using contrastive pretraining. The pivotal advantage of such alignment is its capacity to introduce a new paradigm of LLM for drug discovery: by fully utilizing the open vocabulary and compositionality attributes of natural language. To adapt it to a more concrete task, we focus on zero-shot text-guided molecule editing (aka lead optimization). Existing ML-based molecule editing methods suffer from data insufficiency issues. MoleculeSTM circumvents this by formulating molecule editing as a natural language understanding and interpolation problem, which is much easier to solve under the zero-shot setting. Such a novel paradigm is meaningful for addressing more practical drug discovery challenges. We will have more follow-up works along this LLM for the molecule/drug discovery research line. Please stay tuned! Shengchao Liu Chaowei Xiao Weili Nie Zhuoran Qiao Caltech

Navigating the complexities of #DrugDiscovery has always presented significant challenges, particularly in understanding protein-protein interactions. The introduction of PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), a groundbreaking software developed by researchers at Cleveland Clinic and Cornell University, could be a game changer in our field. By integrating vast genomic data with physical protein structures, PIONEER offers an unprecedented tool for pinpointing crucial interaction points that can be targeted for effective treatments, especially for diseases like cancer. This innovative AI-driven approach not only streamlines the identification of potential drug targets but also addresses the longstanding bottlenecks in drug development timelines. The validation of this tool through extensive laboratory research underscores its potential to impact patient outcomes significantly. As we move forward, I believe tools like these will not only enhance our understanding of complex diseases but also expedite the path to delivering effective treatments to patients in need.

AI just designed a clinically effective antibiotic that works against MRSA. Most generative models in drug discovery propose molecules that can’t be synthesized or validated. That’s changing. 𝗦𝘆𝗻𝘁𝗵𝗲𝗠𝗼𝗹-𝗥𝗟 𝗶𝘀 𝗮 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗴𝗲𝗻���𝗿𝗮𝘁𝗶𝗻𝗴 𝗻𝗼𝘃𝗲𝗹, 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘁𝗿𝗮𝗰𝘁𝗮𝗯𝗹𝗲 𝗮𝗻𝘁𝗶𝗯𝗶𝗼𝘁𝗶𝗰𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.  1. Searched a 46B compound space using RL to optimize antibacterial activity and solubility simultaneously.  2. Outperformed Monte Carlo and virtual screening baselines, generating 11.6% predicted multi-objective hits vs 0.006% for AI-based screening.  3. Synthesized 79 unique AI-designed compounds; 13 showed in vitro potency (MIC ≤ 8 µg/ml), and 7 were structurally novel.  4. Validated one compound, synthecin, in a mouse MRSA wound model, showing full infection suppression and zero tissue inflammation. Couple thoughts:  • Rather than filtering out high-toxicity candidates post-hoc via ADMET-AI, integrating ClinTox predictions into the RL reward could steer generation away from unsafe chemotypes from the outset.  • Feeding back in vitro MIC and solubility results to continuously retrain the RL value models could sharpen predictions in relevant chemical neighborhoods and expedite SAR optimization, leveraging the strong clustering behavior already observed.  • The current maximal independent set method ensures chemical diversity but can be further enhanced by recent GFlowNet-inspired subset selection algorithms to yield larger, more evenly distributed clusters of candidates. Here's the awesome work: https://lnkd.in/gwVNdtqy Congrats to Kyle Swanson, Gary Liu, Denise Catacutan, Stewart McLellan, Autumn Arnold, Jonathan M. Stokes, James Zou and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

📢 A new paper suggests that a plain-language text prompt may soon be enough to launch an end-to-end drug discovery program... In a new paper, co-authored by Alex Zhavoronkov and David Gennert, PhD, (Insilico Medicine) and Jiye Shi (Eli Lilly and Company), researchers conceptualize a drug discovery paradigm in which a text prompt can initiate an end-to-end drug development program, from target discovery to a clinical-ready candidate. In "From Prompt to Drug: Toward Pharmaceutical Superintelligence", the authors describe how modern drug discovery already can benefit from AI at nearly every step, including omics-driven target identification, generative molecular design, docking and ADMET prediction, retrosynthesis planning, automated synthesis, and even clinical trial modeling. ☝ The problem, they argue, is not a lack of capability but a lack of integration. These systems operate in silos, with humans coordinating handoffs between tools, labs, and teams, creating delays, errors, and bias. Their proposed solution is an AI-orchestrated "system-of-systems". Large language models with advanced reasoning capabilities act as central controllers: planning workflows, coordinating specialized AI agents, calling physics-based models (molecular dynamics, docking, QM), and interfacing with automated laboratories via APIs. Rather than generating molecules directly and hoping for the best, the system runs closed-loop design–make–test–analyze cycles, where experimental results continuously feed back into model refinement. The paper is explicit about technical constraints, though. LLMs alone lack biochemical grounding, suffer from hallucinations, and can propagate errors across pipeline stages. To mitigate this, the authors emphasize hybrid architectures combining language-based planning with structure-aware models, ensemble validation between agents, confidence propagation, backtracking, and mandatory human-in-the-loop checkpoints for high-stakes decisions such as clinical trial design. They refer to the long-term outcome as Pharmaceutical Superintelligence. It is not a single model, but a coordinated, multimodal platform trained on omics data, molecular structures, experimental results, and clinical outcomes, capable of autonomously running large portions of drug discovery while remaining auditable and regulator-aligned. It is a thought-provoking read, and I am curious to read your thoughts about it. While the idea might seem futuristic to some, Insilico Medicine demonstrated a track record of fast-paced drug discovery programs reaching clinical milestones... so while none of their programs are FDA approved yet, they are certainly trying hard to build this vision, it seems... time will tell. Image credit: authors of the paper

Structure-based computational approaches are game-changers in drug discovery, but even the best tools can lead us astray if we’re not careful. Here are three common mistakes that lead research teams astray. 1. Skipping Workflow Validation Jumping straight into screening can be tempting, but without validating your workflow, the foundation of your approach may be unstable. Validation ensures your methods are both robust and reliable. Begin with well-characterized datasets to verify your approach before proceeding. When working with a novel target or binding site, consider validating against similar pockets—such as those from related proteins or with comparable pocket characteristics. Taking this extra step strengthens confidence in your results and supports more informed decision-making. 2. Not Sanity Checking Poses Docking software provides automated results, but are the poses realistic? Skipping this sanity check can lead to wasted efforts on implausible molecules. Take the time to visually inspect poses, ensuring they align with known binding mechanisms or make sense in the context of your target. 3. Picking Top Compounds for Testing Focusing solely on the top-scoring compounds might seem logical, but it can limit your success. Scoring algorithms have limitations, and a narrow focus could overlook promising candidates. Instead, use diverse selection criteria - consider ligand efficiency, novelty, and chemical diversity to ensure a well-rounded testing set. Every step in the drug discovery process is critical. Overlooking common pitfalls in structure-based virtual screening can result in missed opportunities, wasted resources, or misleading outcomes early in a project - challenges that lead optimization may not be able to fix. A compound with fundamental flaws is often unsalvageable, no matter how much effort is applied downstream. By remaining vigilant and thorough from the outset, we can fully leverage the potential of these powerful tools and set our projects on the path to success. What steps do you take to ensure your structure-based workflows are foolproof? How do you balance automation with manual oversight? #DrugDiscovery #VirtualScreening #MedicinalChemistry #ComputationalChemistry Image: 10.1038/s41467-022-33981-8

“The Logic of Chemical Optimization”   In the course of drug development, compounds undergo multiparameter optimization, a process by which a hit compound evolves into a lead, which, in turn, evolves into a candidate. A hit may have weak affinity for a target, while a lead likely has an increased affinity and may add additional physiochemical attributes; candidates instead add metabolic stability, efficacy, tolerability, safety.   Scientists at Sanofi looked at one of their past drug campaigns totaling 1681 molecules to assess whether one could develop an optimization scheme by working backward from a reported candidate to leads to hits, as it is commonly done in retrosynthesis. Proceeding backward identifies alternative leads and compares them with the actual lead, gauging the efficiency of various optimization paths. In the image below, it is reported the optimization path taken using minimal function group changes from the hit (1) to the actual lead (3) to the candidate (4). The actual optimization path required four matched pairs from hit to lead, and six more from lead to candidate. A search of alternative optimization paths reveals path A (shorter than the actual route taken) and path B (longer, 11 steps).   It is well-reported that lipophilic ligand efficiency correlates with clinical success and that defining the minimum pharmacophore and property-based optimization are best practices in the field of drug discovery. Still, the authors have found that three parameters taken from network analysis field, and not yet used in medicinal chemistry, could help identify the theoretical lead (2): network connectivity (how well various parts of the network connect to one another), betweenness centrality (identifies nodes that serve as bridges between other nodes in the network), edge count (number of connections) and average shortest path length (a measure of the efficiency of information on a network).   Finally, scientists sought to determine the location of functional group changes on the evolving scaffold (structural perturbation) while holding the rest of the structure constant. The structure of the hit is divided into three regions and the percentage of compounds in which a designated region has been perturbed is tracked over time. Initially, it turns out that the optimization campaign focuses on peripheral functional group modification, while later it also involves the core of the structure.   Network analysis run on a total of four projects at Sanofi indicates that while one of such projects obtained the candidate molecule after testing only 10% of the total compounds; instead, it took the other three projects testing 50-79% of the molecules before reaching the candidate.   Post n. 128. Original publication: https://lnkd.in/d3526uyj #chemistry #medicinalchemistry #drugdiscovery #drugdevelopment #pharma #science #research #synthesis

Artificial intelligence in drug development Drug discovery has long struggled with high costs and low success rates, hindered by the complexities of disease mechanisms and the massive chemical space to explore. Recent advances in artificial intelligence (AI)—from large language models (LLMs) to sophisticated machine learning frameworks—aim to overcome these bottlenecks, accelerating the identification of viable drug targets, expediting virtual screening, and optimizing clinical trials. By sifting through extensive multi-omics and real-world datasets, AI-driven methods can decipher intricate biological pathways, predict toxicity, and streamline regulatory compliance in ways that human-led, trial-and-error paradigms cannot match. Zhang and colleagues provide a comprehensive overview of how AI is applied throughout drug development, spanning from target identification and molecular design to preclinical evaluation and clinical monitoring. They discuss how new machine learning tools, such as generative models and specialized LLMs, enable deeper insights into disease biology and chemical diversity, thereby reducing the time and cost of discovering active compounds. Drawing on examples of AI-based activity prediction and drug repurposing, the authors show that even real-world, noisy datasets—such as electronic health records and insurance claims—can yield actionable results for complex conditions, paving the way toward personalized treatments. Their review also highlights remaining hurdles to AI-augmented drug development, including sparse data, interpretability challenges, and the need for more robust cross-industry collaboration. Yet there is confidence that with continued innovation, AI will increasingly function as a co-pilot rather than a mere assistant in modern pharmaceutical research, helping deliver better, safer medications faster. By integrating knowledge-driven approaches and improved data stewardship, the field stands at the threshold of an era in which AI significantly boosts both efficiency and innovation in drug development. Paper: https://lnkd.in/dGmvh6am #DrugDiscovery #AIinPharma #MachineLearning #GenerativeAI #Biotech #DrugDevelopment #LLMs #PrecisionMedicine #ClinicalTrials #AIforScience #Pharmaceuticals #MultiOmics #RealWorldData #HealthcareInnovation #BiomedicalResearch

🚀 Pushing the boundaries of AI in drug discovery! 🧬 “Large Language Model Agent for Modular Task Execution in Drug Discovery” — now on bioRxiv! In this work, we introduce AgentD, an LLM-powered agent that integrates language reasoning with domain-specific tools to automate and streamline the early-stage drug discovery pipeline. ✨ What can AgentD do? Retrieve biomedical data (FASTA sequences, SMILES, literature) from web and structured databases. Answer tough, domain-specific scientific questions grounded in real literature (via RAG). Generate diverse seed molecules (using REINVENT & Mol2Mol). Predict critical ADMET properties and binding affinities. Iteratively refine molecules to improve drug-likeness and safety. Generate 3D protein–ligand complex structures for deeper analysis. 🚀 Why is this exciting? Drug discovery typically takes 10–15 years and billions of dollars. AgentD tackles these bottlenecks by integrating all the pieces into one modular, flexible, LLM-driven framework — enabling rapid screening, prioritization, and structural evaluation of drug candidates. In our case study on BCL-2 for lymphocytic leukemia: ✅ Increased drug-likeness (QED > 0.6) from 34 to 55 molecules after just two refinement rounds. ✅ Boosted compounds satisfying empirical drug-likeness rules from 29 to 52. ✅ Generated 3D structures to prepare for docking and MD — all starting from a single query. The modular design means AgentD can easily incorporate new generative models, property predictors, and simulation tools, making it a robust foundation for next-generation AI-driven therapeutic discovery. 📖 Check out the preprint here: https://lnkd.in/eysCq2_A #DrugDiscovery #AI #LargeLanguageModels #ComputationalBiology #GenerativeAI #MachineLearning #PharmaTech #LLM #Bioinformatics #CMU

This new study by Rácz et al. (Nature Reviews Drug Discovery) confirms what I’ve been seeing in projects for a while now. The bar for early-stage compounds keeps rising. To track shifts in design priorities, the authors analyzed hit–lead–candidate trajectories for >440 oral small molecules from literature (J. Med. Chem., 2000–2022) and industry (AstraZeneca, Novartis). Each triplet was assessed for potency, property evolution, and design strategy. 🔍 Key insights: Modern hit–lead–candidate trajectories now start with compounds already exhibiting high potency and balanced polarity, then progressively tighten developability parameters, particularly by dialing back lipophilicity. Traditional lead‑like cutoffs (MW < 350, logP < 3) apply to fewer than half of leads today, yet many “over‑threshold” molecules succeed thanks to front‑loaded ADMET profiling. While ~70 % of candidates still conform to Ro5, an expanding fraction embraces the extended Ro5 space, trading size and complexity for permeability and oral bioavailability. It means the industry continuously de‑risks pipelines by investing more in early multiparameter profiling, while still meeting ever‑tighter timelines. For example, in Lilly’s CDK4/6 program, the team started with a 2‑anilino‑pyrimidine scaffold already delivering low‑nanomolar CDK4/6 inhibition, then from the very first analogs tracked lipophilic ligand efficiency. By holding logP in check and flagging ADMET concerns alongside structure–activity work, they progressed to abemaciclib (LY‑2835219), a molecule with low‑nanomolar potency, solid permeability, and metabolic stability, which received FDA approval in 2017. I’ve come to see that clinical viability isn’t something you optimize downstream: it has to shape the very first design cycle. That’s why we embed potency, ADMET, and selectivity constraints from the outset and refine every step through experimental feedback. Paper: https://lnkd.in/e-EH_tRB #drugdiscovery #biotech #pharma